Publications by authors named "Hongfang Liu"

381 Publications

Characterizing Long COVID: Deep Phenotype of a Complex Condition.

EBioMedicine 2021 Nov 25;74:103722. Epub 2021 Nov 25.

Department of Diagnostic and Health Sciences, University of Tennessee Health Science Center, 920 Madison Ave. Suite 518N, Memphis TN 38613.

Background: Numerous publications describe the clinical manifestations of post-acute sequelae of SARS-CoV-2 (PASC or "long COVID"), but they are difficult to integrate because of heterogeneous methods and the lack of a standard for denoting the many phenotypic manifestations. Patient-led studies are of particular importance for understanding the natural history of COVID-19, but integration is hampered because they often use different terms to describe the same symptom or condition. This significant disparity in patient versus clinical characterization motivated the proposed ontological approach to specifying manifestations, which will improve capture and integration of future long COVID studies.

Methods: The Human Phenotype Ontology (HPO) is a widely used standard for exchange and analysis of phenotypic abnormalities in human disease but has not yet been applied to the analysis of COVID-19.

Findings: We identified 303 articles published before April 29, 2021, curated 59 relevant manuscripts that described clinical manifestations in 81 cohorts three weeks or more following acute COVID-19, and mapped 287 unique clinical findings to HPO terms. We present layperson synonyms and definitions that can be used to link patient self-report questionnaires to standard medical terminology. Long COVID clinical manifestations are not assessed consistently across studies, and most manifestations have been reported with a wide range of synonyms by different authors. Across at least 10 cohorts, authors reported 31 unique clinical features corresponding to HPO terms; the most commonly reported feature was Fatigue (median 45.1%) and the least commonly reported was Nausea (median 3.9%), but the reported percentages varied widely between studies.

Interpretation: Translating long COVID manifestations into computable HPO terms will improve analysis, data capture, and classification of long COVID patients. If researchers, clinicians, and patients share a common language, then studies can be compared/pooled more effectively. Furthermore, mapping lay terminology to HPO will help patients assist clinicians and researchers in creating phenotypic characterizations that are computationally accessible, thereby improving the stratification, diagnosis, and treatment of long COVID.

Funding: U24TR002306; UL1TR001439; P30AG024832; GBMF4552; R01HG010067; UL1TR002535; K23HL128909; UL1TR002389; K99GM145411 .
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http://dx.doi.org/10.1016/j.ebiom.2021.103722DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8613500PMC
November 2021

Artificial Intelligence Assesses Clinician's Adherence to Asthma Guidelines using Electronic Health Records.

J Allergy Clin Immunol Pract 2021 Nov 17. Epub 2021 Nov 17.

Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester MN. Electronic address:

Background: Clinician's asthma guideline adherence in asthma care is suboptimal. The effort for improving adherence can be enhanced by assessing and monitoring clinician's adherence to guidelines reflected in electronic health records (EHRs) which yet requires costly manual chart review since many care elements cannot be identified by structured data.

Objective: This study was designed to demonstrate the feasibility of an artificial intelligence (AI) tool using natural language processing (NLP) leveraging free text of EHRs of pediatric patients to extract key components of the 2007 National Asthma Education and Prevention Program guidelines.

Methods: This is a retrospective cross-sectional study using a birth cohort with asthma diagnosis at Mayo Clinic between 2003 and 2016. We used 1,039 clinical notes with an asthma diagnosis from a random sample of 300 patients. Rule-based NLP algorithms were developed to identify asthma guideline congruent elements by examining care description in EHR free text.

Results: NLP algorithms demonstrated a sensitivity (0.82 - 1.0), specificity (0.95 - 1.0), positive predictive value (0.86 -1.0), and negative protective value (0.92 - 1.0) against manual chart review for asthma guideline-congruent elements. Assessing medication compliance and inhaler technique assessment were the most challenging elements to assess due to the complexity and wide variety of descriptions.

Conclusion: NLP technologies may enable automated assessment of clinician's documentation in EHRs regarding adherence to asthma guidelines and can be a useful population management and research tool for assessing and monitoring asthma care quality. Multi-site studies with a larger sample size are needed for assessing the generalizability of our NLP algorithms.
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http://dx.doi.org/10.1016/j.jaip.2021.11.004DOI Listing
November 2021

Establishing an expert consensus for the operational definitions of asthma-associated infectious and inflammatory multimorbidities for computational algorithms through a modified Delphi technique.

BMC Med Inform Decis Mak 2021 11 8;21(1):310. Epub 2021 Nov 8.

Precision Population Science Lab, Mayo Clinic, Rochester, MN, USA.

Background: A subgroup of patients with asthma has been reported to have an increased risk for asthma-associated infectious and inflammatory multimorbidities (AIMs). To systematically investigate the association of asthma with AIMs using a large patient cohort, it is desired to leverage a broad range of electronic health record (EHR) data sources to automatically identify AIMs accurately and efficiently.

Methods: We established an expert consensus for an operational definition for each AIM from EHR through a modified Delphi technique. A series of questions about the operational definition of 19 AIMS (11 infectious diseases and 8 inflammatory diseases) was generated by a core team of experts who considered feasibility, balance between sensitivity and specificity, and generalizability. Eight internal and 5 external expert panelists were invited to individually complete a series of online questionnaires and provide judgement and feedback throughout three sequential internal rounds and two external rounds. Panelists' responses were collected, descriptive statistics tabulated, and results reported back to the entire group. Following each round the core team of experts made iterative edits to the operational definitions until a moderate (≥ 60%) or strong (≥ 80%) level of consensus among the panel was achieved.

Results: Response rates for each Delphi round were 100% in all 5 rounds with the achievement of the following consensus levels: (1) Internal panel consensus: 100% for 8 definitions, 88% for 10 definitions, and 75% for 1 definition, (2) External panel consensus: 100% for 12 definitions and 80% for 7 definitions.

Conclusions: The final operational definitions of AIMs established through a modified Delphi technique can serve as a foundation for developing computational algorithms to automatically identify AIMs from EHRs to enable large scale research studies on patient's multimorbidities associated with asthma.
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http://dx.doi.org/10.1186/s12911-021-01663-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8573872PMC
November 2021

Crevice corrosion of X80 carbon steel induced by sulfate reducing bacteria in simulated seawater.

Bioelectrochemistry 2021 Dec 17;142:107933. Epub 2021 Aug 17.

Key Laboratory of Material Chemistry for Energy Conversion and Storage, Ministry of Education, Hubei Key Laboratory of Material Chemistry and Service Failure, Hubei Engineering Research Center for Biomaterials and Medical Protective Materials, School of Chemistry and Chemical Engineering, Huazhong University of Science and Technology, Wuhan 430074, PR China. Electronic address:

Crevice corrosion of X80 carbon steel in simulated seawater with the presence of SRB was studied by surface analysis and electrochemical measurements. The electrode inside crevice was seriously corroded. Large amount of corrosion products accumulated along the crevice mouth. Galvanic current densities measurements confirmed that there was a galvanic effect between the carbon steel at the crevice interior and exterior during the crevice corrosion. The difference in the sessile SRB cells quantities and SRB biofilms developments inside and outside crevice caused the galvanic effect between the carbon steel inside and outside the crevice, which further induced crevice corrosion. Increased crevice width reduced the galvanic effect, resulting in less crevice corrosion in wider crevice.
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http://dx.doi.org/10.1016/j.bioelechem.2021.107933DOI Listing
December 2021

CRISPR/Cas9-targeted mutagenesis of the BnaA03.BP gene confers semi-dwarf and compact architecture to rapeseed (Brassica napus L.).

Plant Biotechnol J 2021 Dec 20;19(12):2383-2385. Epub 2021 Sep 20.

Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture and Rural Affairs, Wuhan, China.

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http://dx.doi.org/10.1111/pbi.13703DOI Listing
December 2021

Risk, Mechanisms and Implications of Asthma-Associated Infectious and Inflammatory Multimorbidities (AIMs) among Individuals With Asthma: a Systematic Review and a Case Study.

Allergy Asthma Immunol Res 2021 Sep;13(5):697-718

Precision Population Science Lab, Department of Pediatrics and Adolescence Medicine, Mayo Clinic, Rochester, MN, USA.

Our prior work and the work of others have demonstrated that asthma increases the risk of a broad range of both respiratory (, pneumonia and pertussis) and non-respiratory (, zoster and appendicitis) infectious diseases as well as inflammatory diseases (, celiac disease and myocardial infarction [MI]), suggesting the systemic disease nature of asthma and its impact beyond the airways. We call these conditions asthma-associated infectious and inflammatory multimorbidities (AIMs). At present, little is known about why some people with asthma are at high-risk of AIMs, and others are not, to the extent to which controlling asthma reduces the risk of AIMs and which specific therapies mitigate the risk of AIMs. These questions represent a significant knowledge gap in asthma research and unmet needs in asthma care, because there are no guidelines addressing the identification and management of AIMs. This is a systematic review on the association of asthma with the risk of AIMs and a case study to highlight that 1) AIMs are relatively under-recognized conditions, but pose major health threats to people with asthma; 2) AIMs provide insights into immunological and clinical features of asthma as a systemic inflammatory disease beyond a solely chronic airway disease; and 3) it is time to recognize AIMs as a distinctive asthma phenotype in order to advance asthma research and improve asthma care. An improved understanding of AIMs and their underlying mechanisms will bring valuable and new perspectives improving the practice, research, and public health related to asthma.
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http://dx.doi.org/10.4168/aair.2021.13.5.697DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8419637PMC
September 2021

CQL4NLP: Development and Integration of FHIR NLP Extensions in Clinical Quality Language for EHR-driven Phenotyping.

AMIA Jt Summits Transl Sci Proc 2021;2021:624-633. Epub 2021 May 17.

Mayo Clinic, Rochester, MN.

Lack of standardized representation of natural language processing (NLP) components in phenotyping algorithms hinders portability of the phenotyping algorithms and their execution in a high-throughput and reproducible manner. The objective of the study is to develop and evaluate a standard-driven approach - CQL4NLP - that integrates a collection of NLP extensions represented in the HL7 Fast Healthcare Interoperability Resources (FHIR) standard into the clinical quality language (CQL). A minimal NLP data model with 11 NLP-specific data elements was created, including six FHIR NLP extensions. All 11 data elements were identified from their usage in real-world phenotyping algorithms. An NLP ruleset generation mechanism was integrated into the NLP2FHIR pipeline and the NLP rulesets enabled comparable performance for a case study with the identification of obesity comorbidities. The NLP ruleset generation mechanism created a reproducible process for defining the NLP components of a phenotyping algorithm and its execution.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8378647PMC
September 2021

An Examination of the Statistical Laws of Semantic Change in Clinical Notes.

AMIA Jt Summits Transl Sci Proc 2021;2021:515-524. Epub 2021 May 17.

Department of Health Sciences Research, Mayo Clinic, Rochester, MN.

Natural language is continually changing. Given the prevalence of unstructured, free-text clinical notes in the healthcare domain, understanding the aspects of this change is of critical importance to clinical Natural Language Processing (NLP) systems. In this study, we examine two previously described semantic change laws based on word frequency and polysemy, and analyze how they apply to the clinical domain. We also explore a new facet of change: whether domain-specific clinical terms exhibit different change patterns compared to general-purpose English. Using a corpus spanning eighteen years of clinical notes, we find that the previously described laws of semantic change hold for our data set. We also find that domain-specific biomedical terms change faster compared to general English words.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8378619PMC
September 2021

Integration of NLP2FHIR Representation with Deep Learning Models for EHR Phenotyping: A Pilot Study on Obesity Datasets.

AMIA Jt Summits Transl Sci Proc 2021;2021:410-419. Epub 2021 May 17.

Mayo Clinic, Rochester, MN.

HL7 Fast Healthcare Interoperability Resources (FHIR) is one of the current data standards for enabling electronic healthcare information exchange. Previous studies have shown that FHIR is capable of modeling both structured and unstructured data from electronic health records (EHRs). However, the capability of FHIR in enabling clinical data analytics has not been well investigated. The objective of the study is to demonstrate how FHIR-based representation of unstructured EHR data can be ported to deep learning models for text classification in clinical phenotyping. We leverage and extend the NLP2FHIR clinical data normalization pipeline and conduct a case study with two obesity datasets. We tested several deep learning-based text classifiers such as convolutional neural networks, gated recurrent unit, and text graph convolutional networks on both raw text and NLP2FHIR inputs. We found that the combination of NLP2FHIR input and text graph convolutional networks has the highest F1 score. Therefore, FHIR-based deep learning methods has the potential to be leveraged in supporting EHR phenotyping, making the phenotyping algorithms more portable across EHR systems and institutions.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8378603PMC
September 2021

Early Detection of Post-Surgical Complications using Time-series Electronic Health Records.

AMIA Jt Summits Transl Sci Proc 2021;2021:152-160. Epub 2021 May 17.

Division of Digital Health Sciences.

Models predicting health complications are increasingly attempting to reflect the temporally changing nature of patient status. However, both the practice of medicine and electronic health records (EHR) have yet to provide a true longitudinal representation of a patient's medical history as relevant data is often asynchronous and highly missing. To match the stringent requirements of many static time models, time-series data has to be truncated, and missing values in samples have to be filled heuristically. However, these data preprocessing procedures may unconsciously misinterpret real-world data, and eventually lead into failure in practice. In this work, we proposed an augmented gated recurrent unit (GRU), which formulate both missingness and timeline signals into GRU cells. Real patient data of post-operative bleeding (POB) after Colon and Rectal Surgery (CRS) was collected from Mayo Clinic EHR system to evaluate the effectiveness of proposed model. Conventional models were also trained with imputed dataset, in which event missingness or asynchronicity were approximated. The performance of proposed model surpassed current state-of-the-art methods in this POB detection task, indicating our model could be more eligible to handle EHR datasets.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8378618PMC
September 2021

Digital Pathology-based Study of Cell- and Tissue-level Morphologic Features in Serous Borderline Ovarian Tumor and High-grade Serous Ovarian Cancer.

J Pathol Inform 2021 5;12:24. Epub 2021 Jun 5.

Department of Health Science Research, Mayo Clinic, Rochester, MN, USA.

Background: Serous borderline ovarian tumor (SBOT) and high-grade serous ovarian cancer (HGSOC) are two distinct subtypes of epithelial ovarian tumors, with markedly different biologic background, behavior, prognosis, and treatment. However, the histologic diagnosis of serous ovarian tumors can be subjectively variable and labor-intensive as multiple tumor slides/blocks need to be thoroughly examined to search for these features.

Materials And Methods: We developed a novel informatics system to facilitate objective and scalable diagnosis screening for SBOT and HGSOC. The system was built upon Groovy scripts and QuPath to enable interactive annotation and data exchange.

Results: The system was used to successfully detect cellular boundaries and extract an expanded set of cellular features representing cell- and tissue-level characteristics. The performance of cell-level classification for both tumor and stroma cells achieved >90% accuracy. The performance of differentiating HGSOC versus SBOT achieved 91%-95% accuracy for 6485 imaging patches which have sufficient tumor and stroma cells (minimum of ten each) and 97% accuracy for classifying patients when aggregating the results to whole-slide image based on consensus.

Conclusions: Cellular features digitally extracted from pathological images can be used for cell classification and SBOT v. HGSOC differentiation. Introducing digital pathology into ovarian cancer research could be beneficial to discover potential clinical implications. A larger cohort is required to further evaluate the system.
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http://dx.doi.org/10.4103/jpi.jpi_76_20DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8356706PMC
June 2021

A fast, resource efficient, and reliable rule-based system for COVID-19 symptom identification.

JAMIA Open 2021 Jul 7;4(3):ooab070. Epub 2021 Aug 7.

Department of Pharmaceutical Care and Health Systems, University of Minnesota, Minneapolis, Minnesota, USA.

Objective: With COVID-19, there was a need for a rapidly scalable annotation system that facilitated real-time integration with clinical decision support systems (CDS). Current annotation systems suffer from a high-resource utilization and poor scalability limiting real-world integration with CDS. A potential solution to mitigate these issues is to use the rule-based gazetteer developed at our institution.

Materials And Methods: Performance, resource utilization, and runtime of the rule-based gazetteer were compared with five annotation systems: BioMedICUS, cTAKES, MetaMap, CLAMP, and MedTagger.

Results: This rule-based gazetteer was the fastest, had a low resource footprint, and similar performance for weighted microaverage and macroaverage measures of precision, recall, and f1-score compared to other annotation systems.

Discussion: Opportunities to increase its performance include fine-tuning lexical rules for symptom identification. Additionally, it could run on multiple compute nodes for faster runtime.

Conclusion: This rule-based gazetteer overcame key technical limitations facilitating real-time symptomatology identification for COVID-19 and integration of unstructured data elements into our CDS. It is ideal for large-scale deployment across a wide variety of healthcare settings for surveillance of acute COVID-19 symptoms for integration into prognostic modeling. Such a system is currently being leveraged for monitoring of postacute sequelae of COVID-19 (PASC) progression in COVID-19 survivors. This study conducted the first in-depth analysis and developed a rule-based gazetteer for COVID-19 symptom extraction with the following key features: low processor and memory utilization, faster runtime, and similar weighted microaverage and macroaverage measures for precision, recall, and f1-score compared to industry-standard annotation systems.
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http://dx.doi.org/10.1093/jamiaopen/ooab070DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8374371PMC
July 2021

Turning the Page: Advancing Detection Platforms for Sulfate Reducing Bacteria and their Perks.

Chem Rec 2021 Aug 20. Epub 2021 Aug 20.

Key Laboratory of Material Chemistry for Energy Conversion and Storage, Ministry of Education, Hubei Key Laboratory of Material Chemistry and Service Failure, School of Chemistry and Chemical Engineering, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China.

Sulfate reducing bacteria (SRB) are blamed as main culprits in triggering huge corrosion damages by microbiologically influenced corrosion. They obtained their energy through enzymatic conversion of sulfates to sulfides which are highly corrosive. However, conventional SRB detection methods are complex, time-consuming and are not enough sensitive for reliable detection. The advanced biosensing technologies capable of overcoming the aforementioned drawbacks are in demand. So, nanomaterials being economical, environmental friendly and showing good electrocatalytic properties are promising candidates for electrochemical detection of SRB as compared with antibody based assays. Here, we summarize the recent advances in the detection of SRB using different techniques such as PCR, UV visible method, fluorometric method, immunosensors, electrochemical sensors and photoelectrochemical sensors. We also discuss the SRB detection based on determination of sulfide, typical metabolic product of SRB.
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http://dx.doi.org/10.1002/tcr.202100166DOI Listing
August 2021

Association of Silent Cerebrovascular Disease Identified Using Natural Language Processing and Future Ischemic Stroke.

Neurology 2021 09 10;97(13):e1313-e1321. Epub 2021 Aug 10.

From the Predictive Analytics and Comparative Effectiveness Center (D.M.K., J.N.) and Department of Neurology (L.Y.L.), Tufts Medical Center, Boston, MA; Department of Research and Evaluation (Y.Z., C.Z., W.C.), Kaiser Permanente Southern California, Pasadena; and Department of Radiology (P.H.L., D.F.K.) and Division of Digital Health Services, Department of Health Sciences Research (S.F., H.L.), Mayo Clinic, Rochester, MN.

Background And Objectives: Silent cerebrovascular disease (SCD), comprising silent brain infarction (SBI) and white matter disease (WMD), is commonly found incidentally on neuroimaging scans obtained in routine clinical care. Their prognostic significance is not known. We aimed to estimate the incidence of and risk increase in future stroke in patients with incidentally discovered SCD.

Methods: Patients in the Kaiser Permanente Southern California (KPSC) health system aged ≥50 years, without prior ischemic stroke, transient ischemic attack (TIA), or dementia/Alzheimer disease receiving a head CT or MRI between 2009 and 2019 were included. SBI and WMD were identified by natural language processing (NLP) from the neuroimage report.

Results: Among 262,875 individuals receiving neuroimaging, NLP identified 13,154 (5.0%) with SBI and 78,330 (29.8%) with WMD. The incidence of future stroke was 32.5 (95% confidence interval [CI] 31.1, 33.9) per 1,000 patient-years for patients with SBI: 19.3 (95% CI 18.9, 19.8) for patients with WMD and 6.8 (95% CI 6.7, 7.0) for patients without SCD. The crude hazard ratio (HR) associated with SBI was 3.40 (95% CI 3.25 to 3.56) and for WMD 2.63 (95% CI 2.54 to 2.71). With MRI-discovered SBI, the adjusted HR was 2.95 (95% CI 2.53 to 3.44) for those <65 years of age and 2.15 (95% CI 1.91 to 2.41) for those ≥65. With CT scan, the adjusted HR was 2.48 (95% CI 2.19 to 2.81) for those <65 and 1.81 (95% CI 1.71 to 1.91) for those ≥65. The adjusted HR associated with a finding of WMD was 1.76 (95% CI 1.69 to 1.82) and was not modified by age or imaging modality.

Discussion: Incidentally discovered SBI and WMD are common and associated with increased risk of subsequent symptomatic stroke, representing an important opportunity for stroke prevention.
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http://dx.doi.org/10.1212/WNL.0000000000012602DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8480402PMC
September 2021

Artificial intelligence-assisted clinical decision support for childhood asthma management: A randomized clinical trial.

PLoS One 2021 2;16(8):e0255261. Epub 2021 Aug 2.

Precision Population Science Lab, Mayo Clinic, Rochester, Minnesota, United States of America.

Rationale: Clinical decision support (CDS) tools leveraging electronic health records (EHRs) have been an approach for addressing challenges in asthma care but remain under-studied through clinical trials.

Objectives: To assess the effectiveness and efficiency of Asthma-Guidance and Prediction System (A-GPS), an Artificial Intelligence (AI)-assisted CDS tool, in optimizing asthma management through a randomized clinical trial (RCT).

Methods: This was a single-center pragmatic RCT with a stratified randomization design conducted for one year in the primary care pediatric practice of the Mayo Clinic, MN. Children (<18 years) diagnosed with asthma receiving care at the study site were enrolled along with their 42 primary care providers. Study subjects were stratified into three strata (based on asthma severity, asthma care status, and asthma diagnosis) and were blinded to the assigned groups.

Measurements: Intervention was a quarterly A-GPS report to clinicians including relevant clinical information for asthma management from EHRs and machine learning-based prediction for risk of asthma exacerbation (AE). Primary endpoint was the occurrence of AE within 1 year and secondary outcomes included time required for clinicians to review EHRs for asthma management.

Main Results: Out of 555 participants invited to the study, 184 consented for the study and were randomized (90 in intervention and 94 in control group). Median age of 184 participants was 8.5 years. While the proportion of children with AE in both groups decreased from the baseline (P = 0.042), there was no difference in AE frequency between the two groups (12% for the intervention group vs. 15% for the control group, Odds Ratio: 0.82; 95%CI 0.374-1.96; P = 0.626) during the study period. For the secondary end points, A-GPS intervention, however, significantly reduced time for reviewing EHRs for asthma management of each participant (median: 3.5 min, IQR: 2-5), compared to usual care without A-GPS (median: 11.3 min, IQR: 6.3-15); p<0.001). Mean health care costs with 95%CI of children during the trial (compared to before the trial) in the intervention group were lower than those in the control group (-$1,036 [-$2177, $44] for the intervention group vs. +$80 [-$841, $1000] for the control group), though there was no significant difference (p = 0.12). Among those who experienced the first AE during the study period (n = 25), those in the intervention group had timelier follow up by the clinical care team compared to those in the control group but no significant difference was found (HR = 1.93; 95% CI: 0.82-1.45, P = 0.10). There was no difference in the proportion of duration when patients had well-controlled asthma during the study period between the intervention and the control groups.

Conclusions: While A-GPS-based intervention showed similar reduction in AE events to usual care, it might reduce clinicians' burden for EHRs review resulting in efficient asthma management. A larger RCT is needed for further studying the findings.

Trial Registration: ClinicalTrials.gov Identifier: NCT02865967.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0255261PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8328289PMC
November 2021

Inhibition of USP11 sensitizes gastric cancer to chemotherapy via suppressing RhoA and Ras-mediated signaling pathways.

Clin Res Hepatol Gastroenterol 2021 Jul 29;46(1):101779. Epub 2021 Jul 29.

Department of Oncology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, People's Republic of China. Electronic address:

Background: The poor outcomes in advanced gastric cancer (GC) necessitate alternative therapeutic strategy. Ubiquitin-specific protease 11 (USP11) has recently garnered attention as a therapeutic target in cancer because of its important regulatory role in cancer cell functions. Here, we revealed the expression, function and underlying molecular interactions of USP11 in gastric cancer.

Methods: The expression of USP11 was analyzed using immunohistochemistry and ELISA. The loss-of function and gain-of function analysis of USP11 was performed using siRNA knockdown and plasmid overexpression approaches. The downstream molecules regulated by USP11 were determined using immunoblotting analysis.

Results: USP11 was upregulated in ∼80% of gastric cancer patients, and the upregulation was associated with HER3 overexpression. In addition, USP11 level was not regulated by HER3 and vice versa. Functional studies demonstrated that USP11 overexpression promoted gastric cancer growth and migration, and alleviated toxicity-induced by chemotherapeutic drug. In contrast, USP11 depletion significantly inhibited gastric cancer growth, migration and survival, and augmented chemotherapeutic drug's efficacy. Gastric cancer cells with higher USP11 levels were more sensitive to USP11 inhibitions than cells with lower USP11 levels. Mechanism studies showed that USP11 depletion suppressed migration via RhoA-mediated pathway and inhibited growth and survival likely via Ras-mediated pathway.

Conclusions: Our work highlights the important role of USP11 in gastric cancer and therapeutic value of inhibiting USP11 to sensitize gastric cancer to chemotherapy.
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http://dx.doi.org/10.1016/j.clinre.2021.101779DOI Listing
July 2021

Are synthetic clinical notes useful for real natural language processing tasks: A case study on clinical entity recognition.

J Am Med Inform Assoc 2021 09;28(10):2193-2201

School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, USA.

Objective: : Developing clinical natural language processing systems often requires access to many clinical documents, which are not widely available to the public due to privacy and security concerns. To address this challenge, we propose to develop methods to generate synthetic clinical notes and evaluate their utility in real clinical natural language processing tasks.

Materials And Methods: : We implemented 4 state-of-the-art text generation models, namely CharRNN, SegGAN, GPT-2, and CTRL, to generate clinical text for the History and Present Illness section. We then manually annotated clinical entities for randomly selected 500 History and Present Illness notes generated from the best-performing algorithm. To compare the utility of natural and synthetic corpora, we trained named entity recognition (NER) models from all 3 corpora and evaluated their performance on 2 independent natural corpora.

Results: : Our evaluation shows GPT-2 achieved the best BLEU (bilingual evaluation understudy) score (with a BLEU-2 of 0.92). NER models trained on synthetic corpus generated by GPT-2 showed slightly better performance on 2 independent corpora: strict F1 scores of 0.709 and 0.748, respectively, when compared with the NER models trained on natural corpus (F1 scores of 0.706 and 0.737, respectively), indicating the good utility of synthetic corpora in clinical NER model development. In addition, we also demonstrated that an augmented method that combines both natural and synthetic corpora achieved better performance than that uses the natural corpus only.

Conclusions: : Recent advances in text generation have made it possible to generate synthetic clinical notes that could be useful for training NER models for information extraction from natural clinical notes, thus lowering the privacy concern and increasing data availability. Further investigation is needed to apply this technology to practice.
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http://dx.doi.org/10.1093/jamia/ocab112DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8449609PMC
September 2021

A novel TLE6 mutation, c.541+1G>A, identified using whole-exome sequencing in a Chinese family with female infertility.

Mol Genet Genomic Med 2021 Aug 15;9(8):e1743. Epub 2021 Jul 15.

The Reproductive Medicine Hospital of the First Hospital of Lanzhou University, Lanzhou, Gansu, China.

Background: Oocytes have a lot of maternal RNAs and proteins, which are used by the early embryo before zygotic genome activation. Transducin-like enhancer of split 6 (TLE6) is a component of a subcortical maternal complex which plays a critical role in early embryonic development.

Methods: The patient had been diagnosed with primary infertility for 6 years and had undergone multiple failed in vitro fertilization (IVF)/intracytoplasmic sperm injection (ICSI) cycles. Genomic DNA samples were extracted from her parents' peripheral blood as well as hers. Whole-exome sequencing and Sanger validation were performed to identify candidate variants.

Results: We identified a novel transducin-like enhancer of split 6 (TLE6) gene mutations in the female patient with recurrent IVF/ICSI failure. The patient carried a homozygous mutation (NM_001143986.1(TLE6): c.541+1G>A) and had viable but low-quality embryos. Her parents both had heterozygous mutations at this locus.

Conclusion: Our study expands the mutational and phenotypic spectrum of TLE6 and suggests the important role of TLE6 during embryonic development. Our findings have implications for the genetic diagnosis of female infertility with recurrent IVF/ICSI failure.
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http://dx.doi.org/10.1002/mgg3.1743DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8404233PMC
August 2021

Using ensemble of ensemble machine learning methods to predict outcomes of cardiac resynchronization.

J Cardiovasc Electrophysiol 2021 09 27;32(9):2504-2514. Epub 2021 Jul 27.

Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.

Introduction: The efficacy of cardiac resynchronization therapy (CRT) has been widely studied in the medical literature; however, about 30% of candidates fail to respond to this treatment strategy. Smart computational approaches based on clinical data can help expose hidden patterns useful for identifying CRT responders.

Methods: We retrospectively analyzed the electronic health records of 1664 patients who underwent CRT procedures from January 1, 2002 to December 31, 2017. An ensemble of ensemble (EoE) machine learning (ML) system composed of a supervised and an unsupervised ML layers was developed to generate a prediction model for CRT response.

Results: We compared the performance of EoE against traditional ML methods and the state-of-the-art convolutional neural network (CNN) model trained on raw electrocardiographic (ECG) waveforms. We observed that the models exhibited improvement in performance as more features were incrementally used for training. Using the most comprehensive set of predictors, the performance of the EoE model in terms of the area under the receiver operating characteristic curve and F1-score were 0.76 and 0.73, respectively. Direct application of the CNN model on the raw ECG waveforms did not generate promising results.

Conclusion: The proposed CRT risk calculator effectively discriminates which heart failure (HF) patient is likely to respond to CRT significantly better than using clinical guidelines and traditional ML methods, thus suggesting that the tool can enhanced care management of HF patients by helping to identify high-risk patients.
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http://dx.doi.org/10.1111/jce.15171DOI Listing
September 2021

Early corrosion behavior of X80 pipeline steel in a simulated soil solution containing Desulfovibrio desulfuricans.

Bioelectrochemistry 2021 Oct 29;141:107880. Epub 2021 Jun 29.

Key Laboratory for Large-Format Battery Materials and System, Ministry of Education, Hubei Key Laboratory of Materials Chemistry and Service Failure, School of Chemistry and Chemical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China.

Microbiologically influenced corrosion (MIC) is one of the reasons leading to the service failure of pipelines buried in the soil. The effects of sulfate-reducing bacteria (SRB) on steel corrosion without organic carbon are not clear. In this work, SRB cells were enriched in the simulated soil solution, aiming to study SRB corrosion behavior without organic carbon source using weight loss, electrochemical measurements, and surface analysis. Effects of DO on SRB corrosion were also studied. Results indicate that SRB can survive after 14 days of incubation without organic carbon source, but approximately 90% SRB have died. SRB without organic carbon source could inhibit the uniform corrosion but enhance the pitting corrosion compared with the control specimen. The corrosion rate of the control calculated from weight loss is highest with a value of (0.081 ± 0.013) mm/y. The highest localized corrosion rate of (0.306 ± 0.006) mm/y is obtained with an initial SRB count of 10 cells/mL. The presence of DO influences the steel corrosion process. Oxygen corrosion dominates for the specimens in the absence and presence of SRB with an initial count of 10 cells/mL, while SRB MIC is primary for the specimens with high SRB counts.
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http://dx.doi.org/10.1016/j.bioelechem.2021.107880DOI Listing
October 2021

Quantifying the Importance of COVID-19 Vaccination to Our Future Outlook.

Mayo Clin Proc 2021 07 27;96(7):1890-1895. Epub 2021 Apr 27.

Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN.

Predictive models have played a critical role in local, national, and international response to the COVID-19 pandemic. In the United States, health care systems and governmental agencies have relied on several models, such as the Institute for Health Metrics and Evaluation, Youyang Gu (YYG), Massachusetts Institute of Technology, and Centers for Disease Control and Prevention ensemble, to predict short- and long-term trends in disease activity. The Mayo Clinic Bayesian SIR model, recently made publicly available, has informed Mayo Clinic practice leadership at all sites across the United States and has been shared with Minnesota governmental leadership to help inform critical decisions during the past year. One key to the accuracy of the Mayo Clinic model is its ability to adapt to the constantly changing dynamics of the pandemic and uncertainties of human behavior, such as changes in the rate of contact among the population over time and by geographic location and now new virus variants. The Mayo Clinic model can also be used to forecast COVID-19 trends in different hypothetical worlds in which no vaccine is available, vaccinations are no longer being accepted from this point forward, and 75% of the population is already vaccinated. Surveys indicate that half of American adults are hesitant to receive a COVID-19 vaccine, and lack of understanding of the benefits of vaccination is an important barrier to use. The focus of this paper is to illustrate the stark contrast between these 3 scenarios and to demonstrate, mathematically, the benefit of high vaccine uptake on the future course of the pandemic.
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http://dx.doi.org/10.1016/j.mayocp.2021.04.012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075811PMC
July 2021

Tuning Electrocatalytic Aptitude by Incorporating α-MnO Nanorods in Cu-MOF/rGO/CuO Hybrids: Electrochemical Sensing of Resorcinol for Practical Applications.

ACS Appl Mater Interfaces 2021 Jul 1;13(27):31462-31473. Epub 2021 Jul 1.

Key Laboratory of Material Chemistry for Energy Conversion and Storage, Ministry of Education, Hubei Key Laboratory of Material Chemistry and Service Failure, Hubei Engineering Research Center for Biomaterials and Medical Protective Materials, School of Chemistry and Chemical Engineering, Huazhong University of Science and Technology, Wuhan 430074, P. R. China.

In this study, Cu-MOF/rGO/CuO/α-MnO nanocomposites have been fabricated by a one-step hydrothermal method and used in the voltammetric detection of resorcinol (RS). The poor conductivity of MOFs in the field of electrochemical sensing is still a major challenge. A series of Cu-MOF/rGO/CuO/α-MnO nanocomposites have been synthesized with varying fractions of rGO and with a fixed amount of α-MnO via a facile method. These nanocomposites are well characterized using some sophisticated characterization techniques. The as-prepared nanohybrids have strongly promoted the redox reactions at the electrode surface due to their synergistic effects of improved conductivity, high electrocatalytic activity, an enlarged specific surface area, and a plethora of nanoscale level interfacial collaborations. The electrode modified with Cu-MOF/rGO/CuO/α-MnO has revealed superior electrochemical properties demonstrating linear differential pulse voltammetry (DPV) responses from a 0.2 to 22 μM RS concentration range ( = 0.999). The overall results of this sensing podium have shown excellent stability, good recovery, and a low detection limit of 0.2 μM. With excellent sensing performance achieved, the practicability of the sensor has been evaluated to detect RS in commercial hair color samples as well as in tap water and river water samples. Therefore, we envision that our hybrid nanostructures synthesized by the structural integration strategy will open new horizons in material synthesis and biosensing platforms.
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http://dx.doi.org/10.1021/acsami.1c07067DOI Listing
July 2021

TEMPORARY REMOVAL: Direct Oral Anticoagulants Compared With Dalteparin for Treatment of Cancer-Associated Thrombosis: A Living, Interactive Systematic Review and Network Meta-analysis.

Mayo Clin Proc 2021 Jun 22. Epub 2021 Jun 22.

Mayo Clinic, Rochester, MN.

The publisher regrets that this article has been temporarily removed. A replacement will appear as soon as possible in which the reason for the removal of the article will be specified, or the article will be reinstated. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal.
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http://dx.doi.org/10.1016/j.mayocp.2020.10.041DOI Listing
June 2021

Longitudinal cohorts for harnessing the electronic health record for disease prediction in a US population.

BMJ Open 2021 06 8;11(6):e044353. Epub 2021 Jun 8.

Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA

Purpose: The depth and breadth of clinical data within electronic health record (EHR) systems paired with innovative machine learning methods can be leveraged to identify novel risk factors for complex diseases. However, analysing the EHR is challenging due to complexity and quality of the data. Therefore, we developed large electronic population-based cohorts with comprehensive harmonised and processed EHR data.

Participants: All individuals 30 years of age or older who resided in Olmsted County, Minnesota on 1 January 2006 were identified for the discovery cohort. Algorithms to define a variety of patient characteristics were developed and validated, thus building a comprehensive risk profile for each patient. Patients are followed for incident diseases and ageing-related outcomes. Using the same methods, an independent validation cohort was assembled by identifying all individuals 30 years of age or older who resided in the largely rural 26-county area of southern Minnesota and western Wisconsin on 1 January 2013.

Findings To Date: For the discovery cohort, 76 255 individuals (median age 49; 53% women) were identified from which a total of 9 644 221 laboratory results; 9 513 840 diagnosis codes; 10 924 291 procedure codes; 1 277 231 outpatient drug prescriptions; 966 136 heart rate measurements and 1 159 836 blood pressure (BP) measurements were retrieved during the baseline time period. The most prevalent conditions in this cohort were hyperlipidaemia, hypertension and arthritis. For the validation cohort, 333 460 individuals (median age 54; 52% women) were identified and to date, a total of 19 926 750 diagnosis codes, 10 527 444 heart rate measurements and 7 356 344 BP measurements were retrieved during baseline.

Future Plans: Using advanced machine learning approaches, these electronic cohorts will be used to identify novel sex-specific risk factors for complex diseases. These approaches will allow us to address several challenges with the use of EHR.
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http://dx.doi.org/10.1136/bmjopen-2020-044353DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8190051PMC
June 2021

Agreement between neuroimages and reports for natural language processing-based detection of silent brain infarcts and white matter disease.

BMC Neurol 2021 May 11;21(1):189. Epub 2021 May 11.

Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA.

Background: There are numerous barriers to identifying patients with silent brain infarcts (SBIs) and white matter disease (WMD) in routine clinical care. A natural language processing (NLP) algorithm may identify patients from neuroimaging reports, but it is unclear if these reports contain reliable information on these findings.

Methods: Four radiology residents reviewed 1000 neuroimaging reports (RI) of patients age > 50 years without clinical histories of stroke, TIA, or dementia for the presence, acuity, and location of SBIs, and the presence and severity of WMD. Four neuroradiologists directly reviewed a subsample of 182 images (DR). An NLP algorithm was developed to identify findings in reports. We assessed interrater reliability for DR and RI, and agreement between these two and with NLP.

Results: For DR, interrater reliability was moderate for the presence of SBIs (k = 0.58, 95 % CI 0.46-0.69) and WMD (k = 0.49, 95 % CI 0.35-0.63), and moderate to substantial for characteristics of SBI and WMD. Agreement between DR and RI was substantial for the presence of SBIs and WMD, and fair to substantial for characteristics of SBIs and WMD. Agreement between NLP and DR was substantial for the presence of SBIs (k = 0.64, 95 % CI 0.53-0.76) and moderate (k = 0.52, 95 % CI 0.39-0.65) for the presence of WMD.

Conclusions: Neuroimaging reports in routine care capture the presence of SBIs and WMD. An NLP can identify these findings (comparable to direct imaging review) and can likely be used for cohort identification.
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http://dx.doi.org/10.1186/s12883-021-02221-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8111708PMC
May 2021

Plasmon-driven light harvesting in poly(vinyl alcohol) films for precise surface topography modulation.

Opt Lett 2021 Apr;46(8):1828-1831

Efficient light harvesting is essential for advanced photonic devices. Complex micro/nano surface relief structures can be produced via light-triggered mechanical movement, but limited in organic active molecular units. In this Letter, we propose to embed noble-metal particles into light-inactive polyvinyl alcohol matrix to construct a light harvesting system driven by plasmon for inscription of surface relief gratings. Ultra-small-sized silver nuclei are generated in the polymer by pre-thermal treatment, acting as an accelerator for the subsequent photoinduced particle growth, hydrogen group cleavage, and matrix softening. Based on such properties, a complex plasmonic array carrying ultra-high-density information is achieved with peristrophic multiplexing holography. This Letter paves a bright way to realize data storage, information encryption, and optical microcavity.
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http://dx.doi.org/10.1364/OL.422176DOI Listing
April 2021

A Living, Interactive Systematic Review and Network Meta-analysis of First-line Treatment of Metastatic Renal Cell Carcinoma.

Eur Urol 2021 Dec 3;80(6):712-723. Epub 2021 Apr 3.

Mayo Clinic, Rochester, MN, USA.

Context: Identifying the most effective first-line treatment for metastatic renal cell carcinoma (mRCC) is challenging as rapidly evolving data quickly outdate the existing body of evidence, and current approaches to presenting the evidence in user-friendly formats are fraught with limitations.

Objective: To maintain living evidence for contemporary first-line treatment for previously untreated mRCC.

Evidence Acquisition: We have created a living, interactive systematic review (LISR) and network meta-analysis for first-line treatment of mRCC using data from randomized controlled trials comparing contemporary treatment options with single-agent tyrosine kinase inhibitors. We applied an advanced programming and artificial intelligence-assisted framework for evidence synthesis to create a living search strategy, facilitate screening and data extraction using a graphical user interface, automate the frequentist network meta-analysis, and display results in an interactive manner.

Evidence Synthesis: As of October 22, 2020, the LISR includes data from 14 clinical trials. Baseline characteristics are summarized in an interactive table. The cabozantinib + nivolumab combination (CaboNivo) is ranked the highest for the overall response rate, progression-free survival, and overall survival, whereas ipilimumab + nivolumab (NivoIpi) is ranked the highest for achieving a complete response (CR). NivoIpi, and atezolizumab + bevacizumab (AteBev) were ranked highest (lowest toxicity) and CaboNivo ranked lowest for treatment-related adverse events (AEs). Network meta-analysis results are summarized as interactive tables and plots, GRADE summary-of-findings tables, and evidence maps.

Conclusions: This innovative living and interactive review provides the best current evidence on the comparative effectiveness of multiple treatment options for patients with untreated mRCC. Trial-level comparisons suggest that CaboNivo is likely to cause more AEs but is ranked best for all efficacy outcomes, except NivoIpi offers the best chance of CR. Pembrolizumab + axitinib and NivoIpi are acceptable alternatives, except NivoIpi may not be preferred for patients with favorable risk. Although network meta-analysis provides rankings with statistical adjustments, there are inherent biases in cross-trial comparisons with sparse direct evidence that does not replace randomized comparisons.

Patient Summary: It is challenging to decide the best option among the several treatment combinations of immunotherapy and targeted treatments for newly diagnosed metastatic kidney cancer. We have created interactive evidence summaries of multiple treatment options that present the benefits and harms and evidence certainty for patient-important outcomes. This evidence is updated as soon as new studies are published.
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http://dx.doi.org/10.1016/j.eururo.2021.03.016DOI Listing
December 2021

Chromosomal analysis for embryos from balanced chromosomal rearrangement carriers using next generation sequencing.

Mol Reprod Dev 2021 05 29;88(5):362-370. Epub 2021 Mar 29.

The Reproductive Medicine Hospital of the First Hospital of Lanzhou University, Lanzhou, Gansu, China.

We aimed to use next generation sequencing (NGS) to investigate chromosomal abnormalities in blastocyst trophectoderm (TE) samples, and reproductive outcomes with the different types of chromosomal rearrangements (CR) and for each sex of CR carrier. A total of 1189 blastocyst TE samples were evaluated using NGS to detect chromosomal unbalanced translocations as well as aneuploidy, including blastocytes from 637 blastocysts from carriers of balanced CR and 552 blastocysts from carriers of normal chromosomes. The optimal embryos had lower chromosomal abnormality rates compared to the poor-quality embryos. The experimental group had significantly reduced rates of normal embryos and euploidy, and higher rates of total abnormalities, aneuploidy and unbalanced chromosomal aberrations. Carriers of reciprocal translocations had a reduced rate of normal embryos and an increased percentage of embryos with total abnormalities and unbalanced chromosomal aberrations compared with carriers of Robertsonian translocations. Couples with female carriers of chromosomal abnormalities had significantly reduced rates of normal embryos and euploidy, and a higher percentage of embryos with total abnormalities, aneuploidy, and unbalanced chromosomal aberrations compared with couples of male carriers. Our preimplantation genetic testing (PGT) study identified higher rates of chromosomal abnormalities, including chromosomal unbalanced translocations and aneuploidy, in blastocysts from CR carriers, especially from the female carriers, in a Chinese population. The PGT cycles successfully improved clinical outcomes by increasing the fertilization rate and reducing the early spontaneous abortion rate compared with the in vitro fertilization and intracytoplasmic sperm injection cycles, especially for CR carriers.
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http://dx.doi.org/10.1002/mrd.23469DOI Listing
May 2021

Antifungal Susceptibility Profiles of Olorofim (Formerly F901318) and Currently Available Systemic Antifungals against Mold and Yeast Phases of .

Antimicrob Agents Chemother 2021 05 18;65(6). Epub 2021 May 18.

Molecular Microbiology Section, Laboratory of Clinical Immunology and Microbiology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA

antifungal susceptibility profiling of 32 clinical and environmental isolates recovered from southern China was performed against olorofim and 7 other systemic antifungals, including amphotericin B, 5-flucytosine, posaconazole, voriconazole, caspofungin, and terbinafine, using CLSI methodology. In comparison, olorofim was the most active antifungal agent against both mold and yeast phases of all tested isolates, exhibiting an MIC range, MIC, and MIC of 0.0005 to 0.002 μg/ml, 0.0005 μg/ml, and 0.0005 μg/ml, respectively.
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http://dx.doi.org/10.1128/AAC.00256-21DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8316025PMC
May 2021

Natural Language Processing and Machine Learning for Identifying Incident Stroke From Electronic Health Records: Algorithm Development and Validation.

J Med Internet Res 2021 03 8;23(3):e22951. Epub 2021 Mar 8.

Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States.

Background: Stroke is an important clinical outcome in cardiovascular research. However, the ascertainment of incident stroke is typically accomplished via time-consuming manual chart abstraction. Current phenotyping efforts using electronic health records for stroke focus on case ascertainment rather than incident disease, which requires knowledge of the temporal sequence of events.

Objective: The aim of this study was to develop a machine learning-based phenotyping algorithm for incident stroke ascertainment based on diagnosis codes, procedure codes, and clinical concepts extracted from clinical notes using natural language processing.

Methods: The algorithm was trained and validated using an existing epidemiology cohort consisting of 4914 patients with atrial fibrillation (AF) with manually curated incident stroke events. Various combinations of feature sets and machine learning classifiers were compared. Using a heuristic rule based on the composition of concepts and codes, we further detected the stroke subtype (ischemic stroke/transient ischemic attack or hemorrhagic stroke) of each identified stroke. The algorithm was further validated using a cohort (n=150) stratified sampled from a population in Olmsted County, Minnesota (N=74,314).

Results: Among the 4914 patients with AF, 740 had validated incident stroke events. The best-performing stroke phenotyping algorithm used clinical concepts, diagnosis codes, and procedure codes as features in a random forest classifier. Among patients with stroke codes in the general population sample, the best-performing model achieved a positive predictive value of 86% (43/50; 95% CI 0.74-0.93) and a negative predictive value of 96% (96/100). For subtype identification, we achieved an accuracy of 83% in the AF cohort and 80% in the general population sample.

Conclusions: We developed and validated a machine learning-based algorithm that performed well for identifying incident stroke and for determining type of stroke. The algorithm also performed well on a sample from a general population, further demonstrating its generalizability and potential for adoption by other institutions.
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http://dx.doi.org/10.2196/22951DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7985804PMC
March 2021
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