[Bioinformatics analysis and validation of the interaction between PML protein and TAB1 protein]

OBJECTIVE

To analyze the interaction between PML protein and TAB1 protein using bioinformatic approaches and experimentally verify the results.

METHODS

Using Rosetta software, a 3D model of TAB1 protein was constructed through a comparative modeling approach; the secondary structure of PML protein was retrieved in the PDB database and its crystal structure and 3D structure were resolved. Zdock 3.0.2 software was used to perform protein-protein docking of PML and TAB1, and the best conformation was extracted for molecular structure analysis of the docking model. The interaction between the two proteins was detected using immunoprecipitation in α-MMC-treated M1 inflammatory macrophages.

RESULTS

When 6IMQ of PML was used as the docking site, PML protein formed 3 salt bridges, 6 hydrogen bonds and 6 hydrophobic interactions with TAB1 proteins; when 5YUF of PML was used as the docking site, PML protein formed 1 hydrogen bond, 3 electrostatic interactions and 9 hydrophobic interactions with TAB1 proteins, and both of the docking modes formed good molecular docking and interactions. In the M1 inflammatory macrophages treated with α-MMC for 4 h, positive protein bands of PML and TAB1 were detected in the cell lysates in PML-IP group.

CONCLUSION

PML protein can interact strongly with TAB1 protein.

B cells and tertiary lymphoid structures are associated with survival in papillary thyroid cancer

PURPOSE

The function of B cells in papillary thyroid cancer (PTC) is controversial. The role of B-cell-related tertiary lymphoid structures (TLSs) is still unclear. Whether B cells exert their anti-tumor effect through forming TLS in PTC needs further investigation.

METHODS

We detected the percentage of B cells in PTC tissues by multi-parameter flow cytometry. Paraffin-embedded tumor tissues of 125 PTC patients were collected and stained with Haematoxylin-Eosin (H&E) for inflammatory infiltration analysis in combination with clinical features. Multiplexed immunohistochemistry (mIHC) was performed to verify the TLSs in above inflammatory infiltration. Correlation of B cells and TLSs with prognosis was analyzed using the TCGA database.

RESULTS

We observed that PTC patients with higher expression of B lineage cell genes had improved survival and the percentage of B cells in the PTC tumor tissues was variable. Moreover, PTC tumor tissues with more B cells were surrounded by immune cell aggregates of varying sizes. We furtherly confirmed the immune cell aggregates as TLSs with different maturation stages. By analyzing PTC data from TCGA database, we found the maturation stages of TLSs were associated with genders and clinical stages among PTC patients. Moreover, patients with high TLSs survived longer and had a better prognosis.

CONCLUSION

B cells are associated with the existence of TLSs which have different maturation stages in PTC. Both B cells and TLSs are associated with the survival rate of PTC. These observations indicate that the anti-tumor effects of B cells in PTC are associated with TLSs formation.

[The impact of extended waiting time on tumor regression after neoadjuvant chemoradiotherapy for locally advanced rectal cancer]

Objective: To investigate the influence of extending the waiting time on tumor regression after neoadjuvant chemoradiology (nCRT) in patients with locally advanced rectal cancer (LARC). Methods: Clinicopathological data from 728 LARC patients who completed nCRT treatment at the First Affiliated Hospital, Naval Medical University from January 2012 to December 2021 were collected for retrospective analysis. The primary research endpoint was the sustained complete response (SCR). There were 498 males and 230 females, with an age (M(IQR)) of 58 (15) years (range: 22 to 89 years). Logistic regression models were used to explore whether waiting time was an independent factor affecting SCR. Curve fitting was used to represent the relationship between the cumulative occurrence rate of SCR and the waiting time. The patients were divided into a conventional waiting time group (4 to <12 weeks, n=581) and an extended waiting time group (12 to<20 weeks, n=147). Comparisons regarding tumor regression, organ preservation, and surgical conditions between the two groups were made using the t test, Wilcoxon rank sum test, or χ2 test as appropriate. The Log-rank test was used to elucidate the survival discrepancies between the two groups. Results: The SCR rate of all patients was 21.6% (157/728). The waiting time was an independent influencing factor for SCR, with each additional day corresponding to an OR value of 1.010 (95%CI: 1.001 to 1.020, P=0.031). The cumulative rate of SCR occurrence gradually increased with the extension of waiting time, with the fastest increase between the 10th week. The SCR rate in the extended waiting time group was higher (27.9%(41/147) vs. 20.0%(116/581), χ2=3.901, P=0.048), and the organ preservation rate during the follow-up period was higher (21.1%(31/147) vs. 10.7%(62/581), χ2=10.510, P=0.001). The 3-year local recurrence/regrowth-free survival rates were 94.0% and 91.1%, the 3-year disease-free survival rates were 76.6% and 75.4%, and the 3-year overall survival rates were 95.6% and 92.2% for the conventional and extended waiting time groups, respectively, with no statistical differences in local recurrence/regrowth-free survival, disease-free survival and overall survival between the two groups (χ2=1.878, P=0.171; χ2=0.078, P=0.780; χ2=1.265, P=0.261). Conclusions: An extended waiting time is conducive to tumor regression, and extending the waiting time to 12 to <20 weeks after nCRT can improve the SCR rate and organ preservation rate, without increasing the difficulty of surgery or altering the oncological outcomes of patients.

[Influencing factors of futile recanalization after endovascular therapy in acute ischemic stroke patients with large vessel occlusions]

Objective: To analyze the influencing factors of futile recanalization after endovascular therapy (EVT) in acute ischemic stroke patients with large vessel occlusions (AIS-LVO). Methods: AIS-LVO patients who underwent EVT with successful recanalization between January 2019 and December 2021 in Neurovascular Center of Changhai Hospital of Naval Medical University were retrospectively selected. Modified Rankin scale (mRS) score 3 months after EVT was used as the prognostic evaluation index, and patients with mRS scores≤2 were classified as the meaningful recanalization group and mRS scores 3-6 as the futile recanalization group. The risk factors, National Institutes of Health stroke scale (NIHSS) score, Glasgow coma scale (GCS) score, Alberta Stroke Program Early CT (ASPECT) score, core infarct volume, etc. in both groups were analyzed, and the influencing factors of futile recanalization after EVT were analyzed by multivariate logistic regression. Continuous variables that do not conform to the normal distribution are represented by [M(Q1,Q3)]. Results: A total of 368 patients meeting the inclusion criteria were collected, including 228 males and 140 females, and aged 68 (61, 77) years. There are 196 patients and 172 patients in the meaningful recanalization and futile recanalization groups, respectively, with the rate of futile recanalization 3 months after EVT of 46.74% (172/368). Comparing the general information and risk factors between the two groups found that the age of patients in the futile recanalization group [71 (65, 79) years] was higher than that in the meaningful recanalization group [65 (59, 72) years]. The baseline NIHSS score [18 (14, 22)] and the rate of not achieving modified Thrombolysis in Cerebral Ischemia grade 3 (mTICI 3) reperfusion (36.1%) were higher in the futile recanalization group than those in the meaningful recanalization group [12 (7, 17) and 19.9%]. The baseline GCS score [11 (9, 13)] was lower in the futile recanalization group than that in the meaningful recanalization group [14 (11, 15)]. The core infarct volume in the futile recanalization group [28 (7, 65) ml] was larger than that in the meaningful recanalization group [6 (0, 17) ml]. The ASPECT score [7 (5, 9)] was lower in the futile recanalization group than that in the meaningful recanalization group [9 (7, 10)]. In addition, the proportion of hypertension, atrial fibrillation, general anesthesia, and symptomatic intracranial hemorrhage was higher in the futile recanalization group (all P<0.05). The time from symptom onset to puncture and from symptom onset to reperfusion was longer in the futile recanalization group (both P<0.05). There were statistically significant differences in trial of Org 10172 in acute stroke treatment (TOAST) classification and the site of occluded blood vessels between the two groups (both P<0.05). Multivariate logistic regression indicated that age ≥80 years(OR=1.935,95%CI: 1.168-3.205), baseline NIHSS score (OR=1.999,95%CI: 1.202-3.325), GCS score (OR=2.299,95%CI: 1.386-3.814), previous stroke history (OR=1.977,95%CI: 1.085-3.604), general anesthesia (OR=1.981,95%CI: 1.143-3.435), not achieving grade 3 recanalization (OR=2.846, 95%CI: 1.575-5.143), ASPECT score<6 (OR=2.616, 95%CI: 1.168-5.857), and core infarct volume>70 ml (OR=2.712, 95%CI: 1.130-6.505) were risk factors for futile recanalization. Conclusion: Age≥80 years, previous stroke history, baseline NIHSS score≥20, GCS score≤8, general anesthesia, ASPECT score<6, core infarct volume>70 ml, and failure to achieve Grade 3 recanalization are independent influencing factors for futile recanalization after endovascular therapy in AIS-LVO patients.

[The anatomic zone localization based on biparametric MRI for the prediction of the risk degree of prostate cancer]

Objective: To investigate the anatomic zone localization based on biparametric magnetic resonance imaging (bpMRI) for the prediction of the risk degree in patients with prostate cancer. Methods: A total of 92 patients with prostate cancer confirmed by radical surgery in First Affiliated Hospital, Air Force Medical University, from January 2017 to December 2021 were collected. All patients underwent bpMRI (non-enhanced scan and DWI). According to ISUP grade, those patients were divided into low-risk group [≤grade 2, n=26, aged 71 (64.0, 5.2) years] and high-risk group[≥grade 3, n=66, aged 70.5 (63.0, 74.0) years]. The interobserver consistency test for ADC values was evaluated using the intraclass correlation coefficients (ICC). The differences in total prostate specific antigen (tPSA) between the two groups were compared and the χ² test was used to compare the differences in the risk of prostate cancer in the transitional and peripheral zone. Independent correlation factors for prostate cancer risk were analyzed by logistic regression using high and low risk of prostate cancer as dependent variables, including factors such as anatomical zone, tPSA, apparent diffusion coefficient mean (ADC_mean), apparent diffusion coefficient minimum (ADC_min) and age. Receiver operating characteristic (ROC) curves were plotted to assess the efficacy of the combined models of anatomical zone, tPSA, and anatomical partitioning+tPSA for diagnosing prostate cancer risk. Results: The ICC values of the ADC_mean and ADC_min between the observers were 0.906 and 0.885, respectively, with good agreement. The tPSA in the low-risk group was lower than that in the high-risk group [19.64 (10.29, 35.18) ng/ml vs 72.42 (24.79, 187.98) ng/ml; P<0.001]; the risk of prostate cancer in the peripheral zone was higher than that in the transitional zone, and the difference was statistically significant (P<0.01). Multifactorial regression showed that anatomical zones (OR=0.120, 95%CI:0.029-0.501, P=0.004) and tPSA (OR=1.059, 95%CI:1.022-1.099, P=0.002) were risk factors for prostate cancer risk. The diagnostic efficacy of the combined model (AUC=0.895, 95%CI: 0.831-0.958) was better than the predictive efficacy of the single model for both anatomical partitioning (AUC=0.717, 95%CI:0.597-0.837) and tPSA (AUC=0.801, 95%CI: 0.714-0.887) (Z=3.91, 2.47; all P<0.05). Conclusions: The malignant degree of prostate cancer in peripheral zone was higher than that in transitional zone. Combination of anatomic zone located by bpMRI and tPSA can be used to predict the risk of prostate cancer before surgery, expected to provide support for patients to develop personalized treatment strategies.

Global issues in obstetric anaesthesia: perspectives from South Africa, Japan, China, Latin America and North America

South Africa is classified as a low- and middle-income country, with a complex mixture of resource-rich and resource-limited settings. In the major referral hospitals, the necessary skill level exists for the management of complex challenges. However, this contrasts with the frequently-inadequate skill levels of anaesthesia practitioners in resource-limited environments. In Japan, obstetricians administer anaesthesia for 40% of caesarean deliveries and 80% of labour analgesia. Centralisation of delivery facilities is now occurring and it is expected that obstetric anaesthesiologists will be available 24 h a day in centralised facilities in the future. In China, improvements in women's reproductive, maternal, neonatal, child, and adolescent health are critical government policies. Obstetric anaesthesia, especially labour analgesia, has received unprecedented attention. Chinese obstetric anaesthesiologists are passionate about clinical research, focusing on efficacy, safety, and topical issues. The Latin-American region has different landscapes, people, languages, and cultures, and is one of the world's regions with the most inequality. There are large gaps in research, knowledge, and health services, and the World Federation of Societies of Anaesthesiologists is committed to working with governmental and non-governmental organisations to improve patient care and access to safe anaesthesia. Anaesthesia workforce challenges, exacerbated by coronavirus disease 2019, beset North American healthcare. Pre-existing struggles by governments and decision-makers to improve health care access remain, partly due to unfamiliarity with the role of the anaesthesiologist. In addition to weaknesses in work environments and dated standards of work culture, the work-life balance demanded by new generations of anaesthesiologists must be acknowledged.

PO2RDF: representation of real-world data for precision oncology using resource description framework

Background

Next-generation sequencing provides comprehensive information about individuals’ genetic makeup and is commonplace in precision oncology practice. Due to the heterogeneity of individual patient’s disease conditions and treatment journeys, not all targeted therapies were initiated despite actionable mutations. To better understand and support the clinical decision-making process in precision oncology, there is a need to examine real-world associations between patients’ genetic information and treatment choices.

Methods

To fill the gap of insufficient use of real-world data (RWD) in electronic health records (EHRs), we generated a single Resource Description Framework (RDF) resource, called PO2RDF (precision oncology to RDF), by integrating information regarding genes, variants, diseases, and drugs from genetic reports and EHRs.

Results

There are a total 2,309,014 triples contained in the PO2RDF. Among them, 32,815 triples are related to Gene, 34,695 triples are related to Variant, 8,787 triples are related to Disease, 26,154 triples are related to Drug. We performed two use case analyses to demonstrate the usability of the PO2RDF: (1) we examined real-world associations between EGFR mutations and targeted therapies to confirm existing knowledge and detect off-label use. (2) We examined differences in prognosis for lung cancer patients with/without TP53 mutations.

Conclusions

In conclusion, our work proposed to use RDF to organize and distribute clinical RWD that is otherwise inaccessible externally. Our work serves as a pilot study that will lead to new clinical applications and could ultimately stimulate progress in the field of precision oncology.

POKR: Building a Computable Heterogeneous Knowledge Resource for Precision Oncology

Precision oncology is expected to improve selection of targeted therapies, tailored to individual patients and ultimately improve cancer patients' outcomes. Several cancer genetics knowledge databases have been successfully developed for such purposes, including CIViC and OncoKB, with active community-based curations and scoring of genetic-treatment evidences. Although many studies were conducted based on each knowledge base respectively, the integrative analysis across both knowledge bases remains largely unexplored. Thus, there exists an urgent need for a heterogeneous precision oncology knowledge resource with computational power to support drug repurposing discovery in a timely manner, especially for life-threatening cancer. In this pilot study, we built a heterogeneous precision oncology knowledge resource (POKR) by integrating CIViC and OncoKB, in order to incorporate unique information contained in each knowledge base and make associations amongst biomedical entities (e.g., gene, drug, disease) computable and measurable via training POKR graph embeddings. All the relevant codes, database dump files, and pre-trained POKR embeddings can be accessed through the following URL: https://github.com/shenfc/POKR.

Advances of Drugs Electroanalysis Based on Direct Electrochemical Redox on Electrodes: A Review

The strong development of mankind is inseparable from the proper use of drugs, and the electroanalytical research of drugs occupies an important position in the field of analytical chemistry. This review mainly elaborates the research progress of drugs electroanalysis based on direct electrochemical redox on various electrodes for the recent decade from 2011 to 2021. At first, we summarize some frequently used electrochemical data processing and electrochemical mechanism research derivation methods in the literature. Then, according to the drug therapeutic and application/usage purposes, the research progress of drugs electrochemical analysis is classified and discussed, where we focus on drugs electrochemical reaction mechanism. At the same time, the comparisons of electrochemical sensing performance of the drugs on various electrodes from recent studies are listed, so that readers can more intuitively compare and understand the electroanalytical sensing performance of each modified electrode for each of the drug. Finally, this review discusses the shortcomings and prospects of the drugs electroanalysis based on direct electrochemical redox research.

Development and validation of a three-immune-related gene signature prognostic risk model in papillary thyroid carcinoma

PURPOSE

Increasing evidence indicates that there is a correlation between papillary thyroid carcinoma (PTC) prognosis and the immune signature. Our goal was to construct a new prognostic tool based on immune genes to achieve more accurate prognosis predictions and earlier diagnoses of PTC.

METHODS

The 493 PTCs samples and 58 tumor-adjacent normal tissues were obtained from The Cancer Genome Atlas database (TCGA). Immune genes were obtained from the ImmPort database. First, this cohort was randomly divided into training cohort and testing cohort. Second, the differentially expressed (DE) immune genes from the training set were used to construct the prognostic model. Then, the testing and entire data cohorts were used to validate the model, and the data were analyzed to determine the correlation of the clinical prognostic model with immune cell infiltration and expression profiles of human leukocyte antigen (HLA) genes. Finally, an analysis of the gene ontology (GO) annotation was performed.

RESULTS

A total of 189 upregulated and 128 downregulated DE immune genes were identified. We developed and validated a three-immune gene model for PTC that includes Hsp70, NOX5, and FGF23. This model was demonstrated to be an independent prognostic variable. In addition, the overall immune activity of the high-risk group was higher than that of the low-risk group.

CONCLUSIONS

We developed and validated a three-immune gene model for PTC that includes HSPA1A, NOX5, and FGF23. This model can be used as a validated tool to predict outcomes in PTC.

Measurement of e^{+}e^{-} Momentum and Angular Distributions from Linearly Polarized Photon Collisions

The Breit-Wheeler process which produces matter and antimatter from photon collisions is experimentally investigated through the observation of 6085 exclusive electron-positron pairs in ultraperipheral Au+Au collisions at sqrt[s_{NN}]=200  GeV. The measurements reveal a large fourth-order angular modulation of cos4Δϕ=(16.8±2.5)% and smooth invariant mass distribution absent of vector mesons (ϕ, ω, and ρ) at the experimental limit of ≤0.2% of the observed yields. The differential cross section as a function of e^{+}e^{-} pair transverse momentum P_{⊥} peaks at low value with sqrt[⟨P_{⊥}^{2}⟩]=38.1±0.9  MeV and displays a significant centrality dependence. These features are consistent with QED calculations for the collision of linearly polarized photons quantized from the extremely strong electromagnetic fields generated by the highly charged Au nuclei at ultrarelativistic speed. The experimental results have implications for vacuum birefringence and for mapping the magnetic field which is important for emergent QCD phenomena.

Does fact-checking habit promote COVID-19 knowledge during the pandemic? Evidence from China

Objectives

Promoting health knowledge during a public health crisis is essential. This study aims to examine how fact-checking habit influences COVID-19 knowledge in the COVID-19 infodemic.

Study design

This study uses a cross-sectional survey.

Methods

During the early outbreak of COVID-19 in China, we conducted an online survey and collected data from 3000 representative Chinese Internet users. The study measured COVID-19 knowledge as a dependent variable, fact-checking habit as an independent variable, and general science knowledge and negative emotion as moderators. Internet use and several demographic factors were used as control variables. Ordinary least squares (OLS) linear regression analysis was conducted to examine the relationship between fact-checking habit and COVID-19 knowledge as a function of science knowledge and negative emotion.

Results

Fact-checking habit was negatively associated with COVID-19 knowledge, and the relationship was moderated by general science knowledge and negative emotion. For those with less science knowledge or higher levels of negative emotion, COVID-19 knowledge was lower with the increase of experience in fact-checking.

Conclusions

During a pandemic, individuals may not be able to obtain high-quality information, even if they regularly fact-check information, and especially when they lack knowledge about science or are influenced by negative emotion. To promote health knowledge during a public health crisis, basic science literacy must be promoted, and the psychological impact of the crisis on the population must also be considered.

Protective effect of the RNA-binding protein RBM10 in hepatocellular carcinoma

OBJECTIVE

To illustrate the protective effect of RBM10 on hepatocellular carcinoma (HCC) progression and the molecular mechanism.

PATIENTS AND METHODS

RBM10 levels in HCC tissues classified by tumor size and tumor node metastasis (TNM) staging were detected by quantitative real-time polymerase chain reaction (qRT-PCR) Chi-square test was conducted to reveal the relationship between RBM10 level and pathological features in HCC patients. The prognostic potential of RBM10 in HCC was assessed via the Kaplan-Meier method. Overexpression of RBM10 was achieved by transfection of LV-RBM10 in HepG2 and HCC-LM3 cells. Cell counting kit-8 (CCK-8) assay and flow cytometry were carried out to detect viability and apoptosis in HCC cells, respectively. In addition, invasive ability was assessed by transwell assay. Protein level of cleaved-caspase-3 was examined by Western blot. Regulatory effects of RBM10 on protein levels of EGFR, ERK and p-ERK were determined.

RESULTS

RBM10 was downregulated in HCC tissues. Its level was markedly lower in HCC cases with larger tumor size and stage III+IV. Low level of RBM10 predicted poor prognosis in HCC patients. Overexpression of RBM10 suppressed viability and invasiveness in HCC-LM3 and HepG2 cells, but enhanced apoptotic rate and protein level of cleaved-caspase-3. EGFR was upregulated in HCC tissues, which was negatively regulated by RBM10. Overexpression of RBM10 downregulated protein levels of EGFR and p-ERK in HCC-LM3 and HepG2 cells.

CONCLUSIONS

RBM10 is downregulated in HCC tissues, which is favorable to the prognosis in HCC patients. As a tumor suppressor, RBM10 attenuates proliferative and invasive abilities, but drives apoptosis in HCC cells, thus alleviating the progression of HCC.

Recommendations for patient similarity classes: results of the AMIA 2019 workshop on defining patient similarity

Defining patient-to-patient similarity is essential for the development of precision medicine in clinical care and research. Conceptually, the identification of similar patient cohorts appears straightforward; however, universally accepted definitions remain elusive. Simultaneously, an explosion of vendors and published algorithms have emerged and all provide varied levels of functionality in identifying patient similarity categories. To provide clarity and a common framework for patient similarity, a workshop at the American Medical Informatics Association 2019 Annual Meeting was convened. This workshop included invited discussants from academics, the biotechnology industry, the FDA, and private practice oncology groups. Drawing from a broad range of backgrounds, workshop participants were able to coalesce around 4 major patient similarity classes: (1) feature, (2) outcome, (3) exposure, and (4) mixed-class. This perspective expands into these 4 subtypes more critically and offers the medical informatics community a means of communicating their work on this important topic.

Long-read assembly of a Great Dane genome highlights the contribution of GC-rich sequence and mobile elements to canine genomes

Technological advances have allowed improvements in genome reference sequence assemblies. Here, we combined long- and short-read sequence resources to assemble the genome of a female Great Dane dog. This assembly has improved continuity compared to the existing Boxer-derived (CanFam3.1) reference genome. Annotation of the Great Dane assembly identified 22,182 protein-coding gene models and 7,049 long noncoding RNAs, including 49 protein-coding genes not present in the CanFam3.1 reference. The Great Dane assembly spans the majority of sequence gaps in the CanFam3.1 reference and illustrates that 2,151 gaps overlap the transcription start site of a predicted protein-coding gene. Moreover, a subset of the resolved gaps, which have an 80.95% median GC content, localize to transcription start sites and recombination hotspots more often than expected by chance, suggesting the stable canine recombinational landscape has shaped genome architecture. Alignment of the Great Dane and CanFam3.1 assemblies identified 16,834 deletions and 15,621 insertions, as well as 2,665 deletions and 3,493 insertions located on secondary contigs. These structural variants are dominated by retrotransposon insertion/deletion polymorphisms and include 16,221 dimorphic canine short interspersed elements (SINECs) and 1,121 dimorphic long interspersed element-1 sequences (LINE-1_Cfs). Analysis of sequences flanking the 3' end of LINE-1_Cfs (i.e., LINE-1_Cf 3'-transductions) suggests multiple retrotransposition-competent LINE-1_Cfs segregate among dog populations. Consistent with this conclusion, we demonstrate that a canine LINE-1_Cf element with intact open reading frames can retrotranspose its own RNA and that of a SINEC_Cf consensus sequence in cultured human cells, implicating ongoing retrotransposon activity as a driver of canine genetic variation.

The significance of Hashimoto's thyroiditis for postoperative complications of thyroid surgery: a systematic review and meta-analysis

INTRODUCTION

Hashimoto's thyroiditis (HT) is one of the most common immune-mediated diseases. It makes thyroid surgery more complicated and difficult because there may be adhesions between the thyroid gland and surrounding structures. However, it is still controversial whether HT patients carry a high risk for postoperative complications of thyroid surgery. The purpose of this study was to investigate the significance of HT for the postoperative complications of thyroid surgery.

METHODS

A search for studies assessing the postoperative complication risks of HT patients compared with that of patients with benign nodules (BNs) was performed in PubMed, EMBASE and Web of Science. Nine studies (20,118 cases, 1,582 cases of HT and 18,536 cases of BN) were identified, and the data from the relevant outcomes were extracted and analysed.

RESULTS

There were no significant differences between the HT group and BN group in recurrent laryngeal nerve palsy (RLNP) and permanent hypoparathyroidism (PHP). The rate of transient hypocalcaemia (THC) was significantly higher in the HT group (16.85%) than in the BN group (13.20%).

CONCLUSIONS

The meta-analysis showed that HT only increased the risk of the postoperative complication THC compared to BN. Understanding the significance of HT in postoperative hypoparathyroidism after thyroid surgery would help clinicians perform sufficient preoperative (and postoperative) assessments and to optimise surgical planning.

Abstract PO-050: Identifying de novo stage IV breast cancer (DNIV) cases in Electronic Health Records (EHR) using natural language processing

Background: DNIV accounts for 6%–10% of newly diagnosed breast cancer cases. Despite widespread mammography screening, its incidence is increasing in the United States and survival of this disease has only modestly improved since the late 1970s. As patient data accumulates in EHR, it’s promising to generate practice-based evidence through utilization of observational data sources. However, assembly of a DNIV cohort based on EHR data is challenging, as key pathologic and staging information are stored in unstructured clinical narratives and not available as structured data. In this study, we developed a rule-based algorithm to phenotype DNIV using natural language processing (NLP) techniques, and implemented the algorithm on our institutional EHR to extract potential DNIV cases. Methods and Results: We defined DNIV as those with either (1) M1 disease identified at time of initial presentation or M1 disease identified within 4 months after definitive surgery. We first developed a reference case list of DNIV verified by physician chart review of the EHR. We next refined the algorithm on a training dataset containing 51 positive and 38 negative reference cases. Next we tested the performance on the testing data containing 23 positive and 55 negative cases. The phenotyping algorithm identified key data elements using NLP, i.e., stage IV breast cancer, definitive surgery, stage 0-III, recurrent breast cancer and associated dates. To identify DNIV cases, phenotyping algorithm integrated temporal relations among the key data elements. The following steps were conducted in the following sequential order: (1) Identification of patients with breast cancer diagnosis using ICD-9 and ICD-10 codes. (2) Patients are positive cases if there are explicit mentions that delineate DNIV from recurrent metastatic breast cancer, such as “de novo stage IV” or “primary intact” detected by NLP at time of diagnosis. (3) Otherwise, patients with definitive surgery are selected if stage IV was within 5 years before definitive surgery or within 4 months after definitive surgery, along with patients with stage IV but without definitive surgery. (4) We further excluded patients with stage 0-III detected within 5 years after stage IV. (5) We further excluded patients with recurrent breast cancer detected before or at time of detection of stage IV. The remaining patients were left as positive cases. Precision of the algorithm was 70%, recall was 87% and F1, the weighted average of precision and recall, was 77%. We implemented our algorithm to interrogate 10 million clinical documents in a cohort of 56,548 patients with breast cancer diagnosis codes who presented to our institution between 2004 and 2018 and had research authorization. We identified 1918 potential DNIV cases. Conclusion: Our future focus is on algorithm refinement. An algorithm-generated cohort could serve as a data source for further study on outcomes related to DNIV and ideally as automated data abstractor and staging process.

Citation Format: Liwei Wang, Karthik Giridhar, Kimberly Corbin, Brenda Ernst, Sadia Choudhery, Emmanuel Gabriel, Feichen Shen, Hongfang Liu, James Jakub. Identifying de novo stage IV breast cancer (DNIV) cases in Electronic Health Records (EHR) using natural language processing [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-050.

Family History Extraction From Synthetic Clinical Narratives Using Natural Language Processing: Overview and Evaluation of a Challenge Data Set and Solutions for the 2019 National NLP Clinical Challenges (n2c2)/Open Health Natural Language Processing (OHNLP) Competition

Background

As a risk factor for many diseases, family history (FH) captures both shared genetic variations and living environments among family members. Though there are several systems focusing on FH extraction using natural language processing (NLP) techniques, the evaluation protocol of such systems has not been standardized.

Objective

The n2c2/OHNLP (National NLP Clinical Challenges/Open Health Natural Language Processing) 2019 FH extraction task aims to encourage the community efforts on a standard evaluation and system development on FH extraction from synthetic clinical narratives.

Methods

We organized the first BioCreative/OHNLP FH extraction shared task in 2018. We continued the shared task in 2019 in collaboration with the n2c2 and OHNLP consortium, and organized the 2019 n2c2/OHNLP FH extraction track. The shared task comprises 2 subtasks. Subtask 1 focuses on identifying family member entities and clinical observations (diseases), and subtask 2 expects the association of the living status, side of the family, and clinical observations with family members to be extracted. Subtask 2 is an end-to-end task which is based on the result of subtask 1. We manually curated the first deidentified clinical narrative from FH sections of clinical notes at Mayo Clinic Rochester, the content of which is highly relevant to patients’ FH.

Results

A total of 17 teams from all over the world participated in the n2c2/OHNLP FH extraction shared task, where 38 runs were submitted for subtask 1 and 21 runs were submitted for subtask 2. For subtask 1, the top 3 runs were generated by Harbin Institute of Technology, ezDI, Inc., and The Medical University of South Carolina with F1 scores of 0.8745, 0.8225, and 0.8130, respectively. For subtask 2, the top 3 runs were from Harbin Institute of Technology, ezDI, Inc., and University of Florida with F1 scores of 0.681, 0.6586, and 0.6544, respectively. The workshop was held in conjunction with the AMIA 2019 Fall Symposium.

Conclusions

A wide variety of methods were used by different teams in both tasks, such as Bidirectional Encoder Representations from Transformers, convolutional neural network, bidirectional long short-term memory, conditional random field, support vector machine, and rule-based strategies. System performances show that relation extraction from FH is a more challenging task when compared to entity identification task.

An aberration detection-based approach for sentinel syndromic surveillance of COVID-19 and other novel influenza-like illnesses

Coronavirus Disease 2019 has emerged as a significant global concern, triggering harsh public health restrictions in a successful bid to curb its exponential growth. As discussion shifts towards relaxation of these restrictions, there is significant concern of second-wave resurgence. The key to managing these outbreaks is early detection and intervention, and yet there is a significant lag time associated with usage of laboratory confirmed cases for surveillance purposes. To address this, syndromic surveillance can be considered to provide a timelier alternative for first-line screening. Existing syndromic surveillance solutions are however typically focused around a known disease and have limited capability to distinguish between outbreaks of individual diseases sharing similar syndromes. This poses a challenge for surveillance of COVID-19 as its active periods tend to overlap temporally with other influenza-like illnesses. In this study we explore performing sentinel syndromic surveillance for COVID-19 and other influenza-like illnesses using a deep learning-based approach. Our methods are based on aberration detection utilizing autoencoders that leverages symptom prevalence distributions to distinguish outbreaks of two ongoing diseases that share similar syndromes, even if they occur concurrently. We first demonstrate that this approach works for detection of outbreaks of influenza, which has known temporal boundaries. We then demonstrate that the autoencoder can be trained to not alert on known and well-managed influenza-like illnesses such as the common cold and influenza. Finally, we applied our approach to 2019-2020 data in the context of a COVID-19 syndromic surveillance task to demonstrate how implementation of such a system could have provided early warning of an outbreak of a novel influenza-like illness that did not match the symptom prevalence profile of influenza and other known influenza-like illnesses.

Genomic Copy Number Variation Study of Nine Macaca Species Provides New Insights into Their Genetic Divergence, Adaptation, and Biomedical Application

Copy number variation (CNV) can promote phenotypic diversification and adaptive evolution. However, the genomic architecture of CNVs among Macaca species remains scarcely reported, and the roles of CNVs in adaptation and evolution of macaques have not been well addressed. Here, we identified and characterized 1,479 genome-wide hetero-specific CNVs across nine Macaca species with bioinformatic methods, along with 26 CNV-dense regions and dozens of lineage-specific CNVs. The genes intersecting CNVs were overrepresented in nutritional metabolism, xenobiotics/drug metabolism, and immune-related pathways. Population-level transcriptome data showed that nearly 46% of CNV genes were differentially expressed across populations and also mainly consisted of metabolic and immune-related genes, which implied the role of CNVs in environmental adaptation of Macaca. Several CNVs overlapping drug metabolism genes were verified with genomic quantitative polymerase chain reaction, suggesting that these macaques may have different drug metabolism features. The CNV-dense regions, including 15 first reported here, represent unstable genomic segments in macaques where biological innovation may evolve. Twelve gains and 40 losses specific to the Barbary macaque contain genes with essential roles in energy homeostasis and immunity defense, inferring the genetic basis of its unique distribution in North Africa. Our study not only elucidated the genetic diversity across Macaca species from the perspective of structural variation but also provided suggestive evidence for the role of CNVs in adaptation and genome evolution. Additionally, our findings provide new insights into the application of diverse macaques to drug study.

The acquisition of Mycobacterium tuberculosis infection in village doctors in China: a prospective study

BACKGROUND: Occupational exposure-related risk of Mycobacterium tuberculosis infection has been reported for village doctors in China. This prospective study aims to estimate the infection acquisition in this key population.METHODS: At baseline, all village doctors registered in Zhongmu County were tested by QuantiFERON^®-TB Gold In-Tube (QFT) and QuantiFERON^®-TB Gold Plus (QFT-Plus) in parallel. Those negatives for either of the tests were retested to identify conversions at the 2-year follow-up investigation.RESULTS: A total of 367 eligible participants completed the 2-year follow-up survey with frequency of conversion of 5.0% (18/361) for QFT and 6.1% (21/343) for QFT-Plus. The agreement of follow-up results between the tests was 93.2% with a κ coefficient of 0.43 (95%CI 0.20-0.65). Among QFT-Plus convertors, the difference between TB1 and TB2 tubes (TB2-TB1) was significantly increased as compared with baseline results (P = 0.039). Participants from the villages with occurrence of microbiologically confirmed pulmonary TB showed higher frequency of QFT conversions (11.0% vs. 3.2%, P = 0.011) and QFT-Plus conversions (12.3% vs. 4.4%, P = 0.027) than those from the villages without occurrence.CONCLUSION: Our results consistently suggest that capability on occupational protection and M. tuberculosis infection control should be improved in village doctors in China.

The 2019 n2c2/OHNLP Track on Clinical Semantic Textual Similarity: Overview

Background

Semantic textual similarity is a common task in the general English domain to assess the degree to which the underlying semantics of 2 text segments are equivalent to each other. Clinical Semantic Textual Similarity (ClinicalSTS) is the semantic textual similarity task in the clinical domain that attempts to measure the degree of semantic equivalence between 2 snippets of clinical text. Due to the frequent use of templates in the Electronic Health Record system, a large amount of redundant text exists in clinical notes, making ClinicalSTS crucial for the secondary use of clinical text in downstream clinical natural language processing applications, such as clinical text summarization, clinical semantics extraction, and clinical information retrieval.

Objective

Our objective was to release ClinicalSTS data sets and to motivate natural language processing and biomedical informatics communities to tackle semantic text similarity tasks in the clinical domain.

Methods

We organized the first BioCreative/OHNLP ClinicalSTS shared task in 2018 by making available a real-world ClinicalSTS data set. We continued the shared task in 2019 in collaboration with National NLP Clinical Challenges (n2c2) and the Open Health Natural Language Processing (OHNLP) consortium and organized the 2019 n2c2/OHNLP ClinicalSTS track. We released a larger ClinicalSTS data set comprising 1642 clinical sentence pairs, including 1068 pairs from the 2018 shared task and 1006 new pairs from 2 electronic health record systems, GE and Epic. We released 80% (1642/2054) of the data to participating teams to develop and fine-tune the semantic textual similarity systems and used the remaining 20% (412/2054) as blind testing to evaluate their systems. The workshop was held in conjunction with the American Medical Informatics Association 2019 Annual Symposium.

Results

Of the 78 international teams that signed on to the n2c2/OHNLP ClinicalSTS shared task, 33 produced a total of 87 valid system submissions. The top 3 systems were generated by IBM Research, the National Center for Biotechnology Information, and the University of Florida, with Pearson correlations of r=.9010, r=.8967, and r=.8864, respectively. Most top-performing systems used state-of-the-art neural language models, such as BERT and XLNet, and state-of-the-art training schemas in deep learning, such as pretraining and fine-tuning schema, and multitask learning. Overall, the participating systems performed better on the Epic sentence pairs than on the GE sentence pairs, despite a much larger portion of the training data being GE sentence pairs.

Conclusions

The 2019 n2c2/OHNLP ClinicalSTS shared task focused on computing semantic similarity for clinical text sentences generated from clinical notes in the real world. It attracted a large number of international teams. The ClinicalSTS shared task could continue to serve as a venue for researchers in natural language processing and medical informatics communities to develop and improve semantic textual similarity techniques for clinical text.

[Interpretation for the group standards in technical specification for health risk investigation of central air conditioning ventilation system during coronavirus disease 2019 epidemic]

The central air conditioning ventilation system plays an important role in the air circulation of buildings such as centralized isolation medical observation points and general public buildings. In order to meet the requirements of COVID-19 epidemic prevention and control, Beijing Preventive Medicine Association organized Beijing CDC and other professional institutes to write up the group standard entitled "Technical specification for health risk investigation of central air conditioning ventilation system during the COVID-19 epidemic (T/BPMA 0006-2020)" . According to the particularity of central air conditioning ventilation system risk control during the outbreak of similar respiratory infectious diseases, based on current laws and regulations and the principle of scientific, practical, consistency and normative, 8 key points of risk investigations were summarized, which were the location of fresh air outlet, air conditioning mode, air return mode, air system, air distribution, fresh air volume, exhaust and air conditioner components. The contents, process, method, data analysis and conclusion of the investigation implementation were also defined and unified. It could standardize and guide institutions such as disease control and health supervision to carry out relevant risk managements, and provided solutions and technical supports for such major public health emergencies in city operations.

von Willebrand factor promotes platelet-induced metastasis of osteosarcoma through activation of the VWF-GPIb axis

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Immunohistochemistry results directly show VWF is increased during tumor progression.

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VWF is expressed as low molecular weight multimer in OS cell line SAOS2.

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VWF promotes platelet-induced metastasis of OS through VWF-GPIb pathway.

von Willebrand factor (VWF) is exclusively expressed in endothelial cells (ECs) and megakaryocytes, which plays a crucial role in the initiation of arterial thrombosis. Recent studies have shown that VWF is also expressed in osteosarcoma (OS) cells and participates in adhesion of cancer cells to platelets, thus promoting metastasis of OS cells. However, it is unclear how OS cell-derived VWF-platelet interaction contributes to the metastasis of OS. We hypothesized that the interaction is mediated by the binding between VWF A1 and GPIbα of platelets, a molecular mechanism similar to that of thrombosis. The increased expression of VWF in SAOS2 cells may contribute to the enhancement of platelet adhesion through the VWF-GPIb pathway, which could promote the migration and invasion capacities of SAOS2 cells in vitro. Antibodies that block the pathway could significantly inhibit the platelet-induced metastasis of OS cells. Our results suggest a theoretical basis for the development of new anti-OS metastasis drugs, and further enrich the mechanism of OS metastasis.

Implementation of a Cohort Retrieval System for Clinical Data Repositories Using the Observational Medical Outcomes Partnership Common Data Model: Proof-of-Concept System Validation

BACKGROUND

Widespread adoption of electronic health records has enabled the secondary use of electronic health record data for clinical research and health care delivery. Natural language processing techniques have shown promise in their capability to extract the information embedded in unstructured clinical data, and information retrieval techniques provide flexible and scalable solutions that can augment natural language processing systems for retrieving and ranking relevant records.

OBJECTIVE

In this paper, we present the implementation of a cohort retrieval system that can execute textual cohort selection queries on both structured data and unstructured text-Cohort Retrieval Enhanced by Analysis of Text from Electronic Health Records (CREATE).

METHODS

CREATE is a proof-of-concept system that leverages a combination of structured queries and information retrieval techniques on natural language processing results to improve cohort retrieval performance using the Observational Medical Outcomes Partnership Common Data Model to enhance model portability. The natural language processing component was used to extract common data model concepts from textual queries. We designed a hierarchical index to support the common data model concept search utilizing information retrieval techniques and frameworks.

RESULTS

Our case study on 5 cohort identification queries, evaluated using the precision at 5 information retrieval metric at both the patient-level and document-level, demonstrates that CREATE achieves a mean precision at 5 of 0.90, which outperforms systems using only structured data or only unstructured text with mean precision at 5 values of 0.54 and 0.74, respectively.

CONCLUSIONS

The implementation and evaluation of Mayo Clinic Biobank data demonstrated that CREATE outperforms cohort retrieval systems that only use one of either structured data or unstructured text in complex textual cohort queries.

Constructing co-occurrence network embeddings to assist association extraction for COVID-19 and other coronavirus infectious diseases

Objective

As coronavirus disease 2019 (COVID-19) started its rapid emergence and gradually transformed into an unprecedented pandemic, the need for having a knowledge repository for the disease became crucial. To address this issue, a new COVID-19 machine-readable dataset known as the COVID-19 Open Research Dataset (CORD-19) has been released. Based on this, our objective was to build a computable co-occurrence network embeddings to assist association detection among COVID-19–related biomedical entities.

Materials and Methods

Leveraging a Linked Data version of CORD-19 (ie, CORD-19-on-FHIR), we first utilized SPARQL to extract co-occurrences among chemicals, diseases, genes, and mutations and build a co-occurrence network. We then trained the representation of the derived co-occurrence network using node2vec with 4 edge embeddings operations (L1, L2, Average, and Hadamard). Six algorithms (decision tree, logistic regression, support vector machine, random forest, naïve Bayes, and multilayer perceptron) were applied to evaluate performance on link prediction. An unsupervised learning strategy was also developed incorporating the t-SNE (t-distributed stochastic neighbor embedding) and DBSCAN (density-based spatial clustering of applications with noise) algorithms for case studies.

Results

The random forest classifier showed the best performance on link prediction across different network embeddings. For edge embeddings generated using the Average operation, random forest achieved the optimal average precision of 0.97 along with a F1 score of 0.90. For unsupervised learning, 63 clusters were formed with silhouette score of 0.128. Significant associations were detected for 5 coronavirus infectious diseases in their corresponding subgroups.

Conclusions

In this study, we constructed COVID-19–centered co-occurrence network embeddings. Results indicated that the generated embeddings were able to extract significant associations for COVID-19 and coronavirus infectious diseases.

The 2019 National Natural language processing (NLP) Clinical Challenges (n2c2)/Open Health NLP (OHNLP) shared task on clinical concept normalization for clinical records

OBJECTIVE

The 2019 National Natural language processing (NLP) Clinical Challenges (n2c2)/Open Health NLP (OHNLP) shared task track 3, focused on medical concept normalization (MCN) in clinical records. This track aimed to assess the state of the art in identifying and matching salient medical concepts to a controlled vocabulary. In this paper, we describe the task, describe the data set used, compare the participating systems, present results, identify the strengths and limitations of the current state of the art, and identify directions for future research.

MATERIALS AND METHODS

Participating teams were provided with narrative discharge summaries in which text spans corresponding to medical concepts were identified. This paper refers to these text spans as mentions. Teams were tasked with normalizing these mentions to concepts, represented by concept unique identifiers, within the Unified Medical Language System. Submitted systems represented 4 broad categories of approaches: cascading dictionary matching, cosine distance, deep learning, and retrieve-and-rank systems. Disambiguation modules were common across all approaches.

RESULTS

A total of 33 teams participated in the MCN task. The best-performing team achieved an accuracy of 0.8526. The median and mean performances among all teams were 0.7733 and 0.7426, respectively.

CONCLUSIONS

Overall performance among the top 10 teams was high. However, several mention types were challenging for all teams. These included mentions requiring disambiguation of misspelled words, acronyms, abbreviations, and mentions with more than 1 possible semantic type. Also challenging were complex mentions of long, multi-word terms that may require new ways of extracting and representing mention meaning, the use of domain knowledge, parse trees, or hand-crafted rules.

Long noncoding RNA DLX6-AS1 functions as a competing endogenous RNA for miR-577 to promote malignant development of colorectal cancer

OBJECTIVE

Recent researches have proved that long noncoding RNAs (lncRNAs) play essential roles in tumorigenesis. The aim of this study was to investigate the exact role of lncRNA DLX6-AS1 in the development of colorectal cancer (CRC), and to explore the possible mechanism.

PATIENTS AND METHODS

DLX6-AS1 expression in CRC tissues was detected by Real Time-quantitative Polymerase Chain Reaction (RT-qPCR). Function assays were conducted to detect the effect of DLX6-AS1 on the proliferation and metastasis of CRC in vitro. Furthermore, luciferase reporter gene assay and RNA immunoprecipitation assay (RIP) were used to explore the underlying mechanism of DLX6-AS1.

RESULTS

DLX6-AS1 expression in CRC samples was significantly higher than that of adjacent tissues. Loss of DLX6-AS1 markedly inhibited the proliferation, migration, and invasion of CRC cells. Furthermore, luciferase reporter gene assay and RIP assay showed that DLX6-AS1 acted as a competing endogenous RNA via sponging miR-577 in CRC.

CONCLUSIONS

DLX6-AS1 could promote the proliferation, migration, and invasion of CRC by sponging miR-577, which might offer a potential therapeutic target for CRC.

Clinical concept extraction: A methodology review

BACKGROUND

Concept extraction, a subdomain of natural language processing (NLP) with a focus on extracting concepts of interest, has been adopted to computationally extract clinical information from text for a wide range of applications ranging from clinical decision support to care quality improvement.

OBJECTIVES

In this literature review, we provide a methodology review of clinical concept extraction, aiming to catalog development processes, available methods and tools, and specific considerations when developing clinical concept extraction applications.

METHODS

Based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a literature search was conducted for retrieving EHR-based information extraction articles written in English and published from January 2009 through June 2019 from Ovid MEDLINE In-Process & Other Non-Indexed Citations, Ovid MEDLINE, Ovid EMBASE, Scopus, Web of Science, and the ACM Digital Library.

RESULTS

A total of 6,686 publications were retrieved. After title and abstract screening, 228 publications were selected. The methods used for developing clinical concept extraction applications were discussed in this review.

Long noncoding RNA DLX6-AS1 functions as a competing endogenous RNA for miR-577 to promote malignant development of colorectal cancer

Since this article has been suspected of research misconduct and the corresponding authors did not respond to our request to prove originality of data and figures, "Long noncoding RNA DLX6-AS1 functions as a competing endogenous RNA for miR-577 to promote malignant development of colorectal cancer, by F.-R. Zhou, Z.-P. Pan, F. Shen, L.-Q. Huang, J.-H. Cui, K. Cai, X.-L. Guo, published in Eur Rev Med Pharmacol Sci 2019; 23 (9): 3742-3748-DOI: 10.26355/eurrev_201905_17800-PMID: 31115000" has been withdrawn. The Publisher apologizes for any inconvenience this may cause. https://www.europeanreview.org/article/17800.

Gender and age differences in the association between sleep characteristics and fasting glucose levels in Chinese adults

AIM

The present study examined the associations between night-time sleep duration, midday napping duration and bedtime, and fasting glucose levels, and whether or not such associations are dependent on gender and age.

METHODS

This study was a cross-sectional analysis of 172,901 adults aged≥40 years living in mainland China. Sleep duration was obtained by self-reports of bedtime at night, waking-up time the next morning and average napping duration at midday. Fasting plasma glucose (FPG)≥7.0mmol/L was defined as hyperglycaemia. Independent associations between night-time sleep duration, midday naptime duration and bedtime with hyperglycaemia were evaluated using regression models.

RESULTS

Compared with night-time sleep durations of 6-7.9h, both short (<6h) and long (≥8h) night-time sleep durations were significantly associated with an increased risk of hyperglycaemia in women [odds ratio (OR): 1.12, 95% confidence interval (CI): 1.01-1.29 and OR: 1.14, 95% CI: 1.08-1.21, respectively], and revealed a U-shaped distribution of risk in women and no significant association in men. Long midday nap durations (≥1h) were significantly but weakly associated with hyperglycaemia (OR: 1.04, 95% CI: 1.01-1.09) compared with no napping without interactions from gender or age, whereas the association between bedtime and fasting glucose levels did vary according to gender and age.

CONCLUSION

Night-time sleep duration, midday napping duration and bedtime were all independently associated with the risk of hyperglycaemia, and some of the associations between these sleep characteristics and hyperglycaemia were gender- and age-dependent.

An Aberration Detection-Based Approach for Sentinel Syndromic Surveillance of COVID-19 and Other Novel Influenza-Like Illnesses

Coronavirus Disease 2019 (COVID-19) has emerged as a significant global concern, triggering harsh public health restrictions in a successful bid to curb its exponential growth. As discussion shifts towards relaxation of these restrictions, there is significant concern of second-wave resurgence. The key to managing these outbreaks is early detection and intervention, and yet there is significant lag time associated with usage of laboratory confirmed cases for surveillance purposes. To address this, syndromic surveillance can be considered to provide a timelier alternative for first-line screening. Existing syndromic surveillance solutions are however typically focused around a known disease and have limited capability to distinguish between outbreaks of individual diseases sharing similar syndromes. This poses a challenge for surveillance of COVID-19 as its active periods are tend to overlap temporally with other influenza-like illnesses. In this study we explore performing sentinel syndromic surveillance for COVID-19 and other influenza-like illnesses using a deep learning-based approach. Our methods are based on aberration detection utilizing autoencoders that leverages symptom prevalence distributions to distinguish outbreaks of two ongoing diseases that share similar syndromes, even if they occur concurrently. We first demonstrate that this approach works for detection of outbreaks of influenza, which has known temporal boundaries. We then demonstrate that the autoencoder can be trained to not alert on known and well-managed influenza-like illnesses such as the common cold and influenza. Finally, we applied our approach to 2019-2020 data in the context of a COVID-19 syndromic surveillance task to demonstrate how implementation of such a system could have provided early warning of an outbreak of a novel influenza-like illness that did not match the symptom prevalence profile of influenza and other known influenza-like illnesses.

Data-driven Sublanguage Analysis for Cancer Genomics Knowledge Modeling: Applications in Mining Oncological Genetics Information from Patients' Genetic Reports

Despite an abundance of information in clinical genetic testing reports, information is oftentimes not well documented/utilized for decision making. Unstructured information in genetic reports can contribute to long-term patient management and future translational research. Thus, we proposed a knowledge model that could manage unstructured information in medical genetic reports and facilitate knowledge extraction, curation and updating. For this pilot study, we used a dataset including 1,565 cancer genetics reports of Mayo Clinic patients. We used a previously developed, data-driven discovery pipeline that involves both semantic annotation and co-occurrence association analysis to establish a knowledge model. We showed that compared to genetic reports, around 56% of testing results are missing or incomplete in the clinical notes. We built a genetic report knowledge model and highlighted four key semantic groups including "Genes and Gene Products" and "Treatments". Coverage of term annotation was 99.5%. Accuracies of term annotation and relationship extraction were 98.9% and 92.9% respectively.

First Measurement of Λ_{c} Baryon Production in Au+Au Collisions at sqrt[s_{NN}]=200 GeV

We report on the first measurement of the charmed baryon Λ_{c}^{±} production at midrapidity (|y|<1) in Au+Au collisions at sqrt[s_{NN}]=200  GeV collected by the STAR experiment at the Relativistic Heavy Ion Collider. The Λ_{c}/D^{0} [denoting (Λ_{c}^{+}+Λ_{c}^{-})/(D^{0}+D[over ¯]^{0})] yield ratio is measured to be 1.08±0.16  (stat)±0.26  (sys) in the 0%-20% most central Au+Au collisions for the transverse momentum (p_{T}) range 3<p_{T}<6  GeV/c. This is significantly larger than the pythia model calculations for p+p collisions. The measured Λ_{c}/D^{0} ratio, as a function of p_{T} and collision centrality, is comparable to the baryon-to-meson ratios for light and strange hadrons in Au+Au collisions. Model calculations including coalescence hadronization for charmed baryon and meson formation reproduce the features of our measured Λ_{c}/D^{0} ratio.

[Recent progress in molecular targeted therapies for intrahepatic cholangiocarcinoma]

Intrahepatic cholangiocarcinoma(ICC) is the second most common primary liver cancer. The incidence of ICC has been significantly increased globally in recent years. The concealed onset of ICC usually results in late disease diagnosis. Liver resection is currently the only well-established treatment for ICC that may cure the disease, however, long-term survival rate is still unsatisfied due to the low resection rate and high recurrence rate. Local therapy combined with systemic chemotherapy is the main treatment for advanced or unresectable ICC, but the outcomes are still poor. With the in-depth understanding of the molecular mechanism of ICC and development of next-generation sequencing technology, multiple abnormal signaling pathways (RAS/MAPK, MET, EGFR) and gene mutations (FGFR2, IDH1/2) have been identified as potential therapeutic targets. Although there is still no approved targeted drugs for ICC, more than 100 clinical trials testing targeted therapy alone or in combination with chemotherapy are ongoing, among which some have shown promising application prospects. Molecular typing and personalized targeted therapy are important ways to improve the overall outcomes of ICC. This review summarized the recent advances in the targeted therapies for patients with ICC.

Dynamic changes of interferon gamma release assay results with latent tuberculosis infection treatment

OBJECTIVES

Using QuantiFERON-TB Gold In-Tube (QFT-GIT) for monitoring tuberculosis (TB) and latent TB infection treatment effect is controversial. The present study aimed to evaluate the dynamic changes of interferon gamma (IFN-γ) levels along with latent TB infection treatment via a randomized controlled study.

METHODS

A total of 910 participants treated with 8 weeks of once-weekly rifapentine plus isoniazid (group A), 890 treated with 6 weeks of twice-weekly rifapentine plus isoniazid (group B) and 818 untreated controls (group C) were followed for 2 years to track active TB development. QFT-GIT tests were repeated three times for all groups: before treatment (T0), at completion of treatment (T1) and 3 months after completion of treatment (T2).

RESULTS

Similar rates of persistent QFT-GIT reversion were observed in groups A (19.0%, 173/910), B (18.5%, 165/890) and C (20.7%, 169/818) (p 0.512). The dynamic changes of IFN-γ levels were not statistically significant among the three groups. In treated participants, individuals with higher baseline IFN-γ levels showed increased TB occurrence (1.0%, 9/896) compared to those with lower baseline levels (0.2%, 2/904) (p 0.037). A similar but statistically insignificant trend was also observed in untreated controls (1.8% (7/400) vs. 0.5% (2/418), p 0.100). When TB cases were matched with non-TB cases on baseline IFN-γ levels, no significant differences were found with respect to the dynamic changes in IFN-γ levels with time, regardless of whether they received treatment.

CONCLUSIONS

QFT-GIT reversion or decreased IFN-γ levels should not be used for monitoring host response to latent TB infection treatment.

Rapid, Paralog-Sensitive CNV Analysis of 2457 Human Genomes Using QuicK-mer2

Gene duplication is a major mechanism for the evolution of gene novelty, and copy-number variation makes a major contribution to inter-individual genetic diversity. However, most approaches for studying copy-number variation rely upon uniquely mapping reads to a genome reference and are unable to distinguish among duplicated sequences. Specialized approaches to interrogate specific paralogs are comparatively slow and have a high degree of computational complexity, limiting their effective application to emerging population-scale data sets. We present QuicK-mer2, a self-contained, mapping-free approach that enables the rapid construction of paralog-specific copy-number maps from short-read sequence data. This approach is based on the tabulation of unique k-mer sequences from short-read data sets, and is able to analyze a 20X coverage human genome in approximately 20 min. We applied our approach to newly released sequence data from the 1000 Genomes Project, constructed paralog-specific copy-number maps from 2457 unrelated individuals, and uncovered copy-number variation of paralogous genes. We identify nine genes where none of the analyzed samples have a copy number of two, 92 genes where the majority of samples have a copy number other than two, and describe rare copy number variation effecting multiple genes at the APOBEC3 locus.

Detecting Lifestyle Risk Factors for Chronic Kidney Disease With Comorbidities: Association Rule Mining Analysis of Web-Based Survey Data

Background

The rise in the number of patients with chronic kidney disease (CKD) and consequent end-stage renal disease necessitating renal replacement therapy has placed a significant strain on health care. The rate of progression of CKD is influenced by both modifiable and unmodifiable risk factors. Identification of modifiable risk factors, such as lifestyle choices, is vital in informing strategies toward renoprotection. Modification of unhealthy lifestyle choices lessens the risk of CKD progression and associated comorbidities, although the lifestyle risk factors and modification strategies may vary with different comorbidities (eg, diabetes, hypertension). However, there are limited studies on suitable lifestyle interventions for CKD patients with comorbidities.

Objective

The objectives of our study are to (1) identify the lifestyle risk factors for CKD with common comorbid chronic conditions using a US nationwide survey in combination with literature mining, and (2) demonstrate the potential effectiveness of association rule mining (ARM) analysis for the aforementioned task, which can be generalized for similar tasks associated with noncommunicable diseases (NCDs).

Methods

We applied ARM to identify lifestyle risk factors for CKD progression with comorbidities (cardiovascular disease, chronic pulmonary disease, rheumatoid arthritis, diabetes, and cancer) using questionnaire data for 450,000 participants collected from the Behavioral Risk Factor Surveillance System (BRFSS) 2017. The BRFSS is a Web-based resource, which includes demographic information, chronic health conditions, fruit and vegetable consumption, and sugar- or salt-related behavior. To enrich the BRFSS questionnaire, the Semantic MEDLINE Database was also mined to identify lifestyle risk factors.

Results

The results suggest that lifestyle modification for CKD varies among different comorbidities. For example, the lifestyle modification of CKD with cardiovascular disease needs to focus on increasing aerobic capacity by improving muscle strength or functional ability. For CKD patients with chronic pulmonary disease or rheumatoid arthritis, lifestyle modification should be high dietary fiber intake and participation in moderate-intensity exercise. Meanwhile, the management of CKD patients with diabetes focuses on exercise and weight loss predominantly.

Conclusions

We have demonstrated the use of ARM to identify lifestyle risk factors for CKD with common comorbid chronic conditions using data from BRFSS 2017. Our methods can be generalized to advance chronic disease management with more focused and optimized lifestyle modification of NCDs.

Ensembles of natural language processing systems for portable phenotyping solutions

BACKGROUND

Manually curating standardized phenotypic concepts such as Human Phenotype Ontology (HPO) terms from narrative text in electronic health records (EHRs) is time consuming and error prone. Natural language processing (NLP) techniques can facilitate automated phenotype extraction and thus improve the efficiency of curating clinical phenotypes from clinical texts. While individual NLP systems can perform well for a single cohort, an ensemble-based method might shed light on increasing the portability of NLP pipelines across different cohorts.

METHODS

We compared four NLP systems, MetaMapLite, MedLEE, ClinPhen and cTAKES, and four ensemble techniques, including intersection, union, majority-voting and machine learning, for extracting generic phenotypic concepts. We addressed two important research questions regarding automated phenotype recognition. First, we evaluated the performance of different approaches in identifying generic phenotypic concepts. Second, we compared the performance of different methods to identify patient-specific phenotypic concepts. To better quantify the effects caused by concept granularity differences on performance, we developed a novel evaluation metric that considered concept hierarchies and frequencies. Each of the approaches was evaluated on a gold standard set of clinical documents annotated by clinical experts. One dataset containing 1,609 concepts derived from 50 clinical notes from two different institutions was used in both evaluations, and an additional dataset of 608 concepts derived from 50 case report abstracts obtained from PubMed was used for evaluation of identifying generic phenotypic concepts only.

RESULTS

For generic phenotypic concept recognition, the top three performers in the NYP/CUIMC dataset are union ensemble (F₁, 0.634), training-based ensemble (F₁, 0.632), and majority vote-based ensemble (F₁, 0.622). In the Mayo dataset, the top three are majority vote-based ensemble (F₁, 0.642), cTAKES (F₁, 0.615), and MedLEE (F₁, 0.559). In the PubMed dataset, the top three are majority vote-based ensemble (F₁, 0.719), training-based (F₁, 0.696) and MetaMapLite (F₁, 0.694). For identifying patient specific phenotypes, the top three performers in the NYP/CUIMC dataset are majority vote-based ensemble (F₁, 0.610), MedLEE (F₁, 0.609), and training-based ensemble (F₁, 0.585). In the Mayo dataset, the top three are majority vote-based ensemble (F₁, 0.604), cTAKES (F₁, 0.531) and MedLEE (F₁, 0.527).

CONCLUSIONS

Our study demonstrates that ensembles of natural language processing can improve both generic phenotypic concept recognition and patient specific phenotypic concept identification over individual systems. Among the individual NLP systems, each individual system performed best when they were applied in the dataset that they were primary designed for. However, combining multiple NLP systems to create an ensemble can generally improve the performance. Specifically, the ensemble can increase the results reproducibility across different cohorts and tasks, and thus provide a more portable phenotyping solution compared to individual NLP systems.

First Observation of the Directed Flow of D^{0} and D^{0}[over ¯] in Au+Au Collisions at sqrt[s_{NN}]=200 GeV

We report the first measurement of rapidity-odd directed flow (v_{1}) for D^{0} and D^{0}[over ¯] mesons at midrapidity (|y|<0.8) in Au+Au collisions at sqrt[s_{NN}]=200  GeV using the STAR detector at the Relativistic Heavy Ion Collider. In 10-80% Au+Au collisions, the slope of the v_{1} rapidity dependence (dv_{1}/dy), averaged over D^{0} and D^{0}[over ¯] mesons, is -0.080±0.017(stat)±0.016(syst) for transverse momentum p_{T} above 1.5  GeV/c. The absolute value of D^{0} meson dv_{1}/dy is about 25 times larger than that for charged kaons, with 3.4σ significance. These data give a unique insight into the initial tilt of the produced matter, and offer constraints on the geometric and transport parameters of the hot QCD medium created in relativistic heavy-ion collisions.

Developing a scalable FHIR-based clinical data normalization pipeline for standardizing and integrating unstructured and structured electronic health record data

Objective

To design, develop, and evaluate a scalable clinical data normalization pipeline for standardizing unstructured electronic health record (EHR) data leveraging the HL7 Fast Healthcare Interoperability Resources (FHIR) specification.

Methods

We established an FHIR-based clinical data normalization pipeline known as NLP2FHIR that mainly comprises: (1) a module for a core natural language processing (NLP) engine with an FHIR-based type system; (2) a module for integrating structured data; and (3) a module for content normalization. We evaluated the FHIR modeling capability focusing on core clinical resources such as Condition, Procedure, MedicationStatement (including Medication), and FamilyMemberHistory using Mayo Clinic’s unstructured EHR data. We constructed a gold standard reusing annotation corpora from previous NLP projects.

Results

A total of 30 mapping rules, 62 normalization rules, and 11 NLP-specific FHIR extensions were created and implemented in the NLP2FHIR pipeline. The elements that need to integrate structured data from each clinical resource were identified. The performance of unstructured data modeling achieved F scores ranging from 0.69 to 0.99 for various FHIR element representations (0.69–0.99 for Condition; 0.75–0.84 for Procedure; 0.71–0.99 for MedicationStatement; and 0.75–0.95 for FamilyMemberHistory).

Conclusion

We demonstrated that the NLP2FHIR pipeline is feasible for modeling unstructured EHR data and integrating structured elements into the model. The outcomes of this work provide standards-based tools of clinical data normalization that is indispensable for enabling portable EHR-driven phenotyping and large-scale data analytics, as well as useful insights for future developments of the FHIR specifications with regard to handling unstructured clinical data.

Polarization of Λ (Λ[over ¯]) Hyperons along the Beam Direction in Au+Au Collisions at sqrt[s_{NN}]=200 GeV

The Λ (Λ[over ¯]) hyperon polarization along the beam direction has been measured in Au+Au collisions at sqrt[s_{NN}]=200  GeV, for the first time in heavy-ion collisions. The polarization dependence on the hyperons' emission angle relative to the elliptic flow plane exhibits a second harmonic sine modulation, indicating a quadrupole pattern of the vorticity component along the beam direction, expected due to elliptic flow. The polarization is found to increase in more peripheral collisions, and shows no strong transverse momentum (p_{T}) dependence at p_{T} greater than 1  GeV/c. The magnitude of the signal is about 5 times smaller than those predicted by hydrodynamic and multiphase transport models; the observed phase of the emission angle dependence is also opposite to these model predictions. In contrast, the kinematic vorticity calculations in the blast-wave model tuned to reproduce particle spectra, elliptic flow, and the azimuthal dependence of the Gaussian source radii measured with the Hanbury Brown-Twiss intensity interferometry technique reproduce well the modulation phase measured in the data and capture the centrality and transverse momentum dependence of the polarization signal.