A comprehensive rat transcriptome built from large scale RNA-seq-based annotation

The rat is an important model organism in biomedical research for studying human disease mechanisms and treatments, but its annotated transcriptome is far from complete. We constructed a Rat Transcriptome Re-annotation named RTR using RNA-seq data from 320 samples in 11 different organs generated by the SEQC consortium. Totally, there are 52 807 genes and 114 152 transcripts in RTR. Transcribed regions and exons in RTR account for ∼42% and ∼6.5% of the genome, respectively. Of all 73 074 newly annotated transcripts in RTR, 34 213 were annotated as high confident coding transcripts and 24 728 as high confident long noncoding transcripts. Different tissues rather than different stages have a significant influence on the expression patterns of transcripts. We also found that 11 715 genes and 15 852 transcripts were expressed in all 11 tissues and that 849 house-keeping genes expressed different isoforms among tissues. This comprehensive transcriptome is freely available at http://www.unimd.org/rtr/. Our new rat transcriptome provides essential reference for genetics and gene expression studies in rat disease and toxicity models.

Development of classification models for predicting inhibition of mitochondrial fusion and fission using machine learning methods

Mitochondrial fusion and fission are processes to maintain mitochondrial function when cells respond to environment stresses. Disruption of mitochondrial fusion and fission influences cell health and can cause adverse events such as neurodegenerative disorders. It is critical to identify environmental chemicals that can disrupt mitochondrial fusion and fission. However, experimentally testing all the chemicals is not practical because experimental methods are time-consuming and costly. Quantitative structure-activity relationship (QSAR) modeling is an attractive approach for evaluation of chemicals disrupting potential on mitochondrial fusion and fission. In this study, QSAR models were developed for differentiating chemicals capable of inhibition of mitochondrial fusion and fission using machine learning algorithms (i.e. random forest, logistic regression, Bernoulli naive Bayes, and deep neural network). One hundred iterations of five-fold cross validations and external validations showed that the best model on mitochondrial fusion had area under the receiver operating characteristic curve (AUC) of 82.8% and 78.1%, respectively; and the best model for mitochondrial fission yielded AUC of 84.3% and 97.5%, respectively. Furthermore, 45 and 56 structural alerts were identified for inhibition of mitochondrial fusion and fission, respectively. The results demonstrated that the models and the structural alerts could be useful for screening chemicals that inhibit mitochondrial fusion and fission.

Reproducibility challenges for biomarker detection with uncertain but informative experimental data

Recent studies have revealed that circulating microRNAs are promising biomarkers for detecting toxicity or disease. Quantitative real-time polymerase chain reaction (qPCR) is often used to measure the levels of microRNAs. Besides complete and certain data, investigators inevitably have observed technically incomplete or uncertain qPCR data. Investigators usually set incomplete observations equal to the maximum quality number of qPCR cycles, apply the complete-observation method, or choose not to analyze targets with incomplete observations. Using biostatistical knowledge and published studies, we show that three commonly applied methods tend to cause biased inference and decrease reproducibility in biomarker detection. More efforts are needed to address the challenges to identify and detect reliable, novel circulating biomarkers in liquid biopsies.

MiR-129 reduces CDDP resistance in gastric cancer cells by inhibiting MAPK3

OBJECTIVE

Abnormal expression of mitogen-activated protein kinase 3 (MAPK3) is related to invasion, metastasis, and drug resistance of multiple tumor cells. MiR-129 expression is associated with gastric cancer. Bioinformatics analysis showed a targeting relation between miR-129 and MAPK3. This study investigated whether miR-129 plays a role in regulating MAPK3 expression, affecting proliferation, apoptosis, and cisplatin (CDDP) resistance of gastric cancer cells.

MATERIALS AND METHODS

The dual-luciferase reporter gene assay was used to assess the targeted regulation between miR-129 and MAPK3. The expression of miR-129 and MAPK3 in CDDP-resistant cell line MGC-803/CDDP and the parental MGC-803 cells was measured. MGC-803/CDDP cells were cultured in vitro and divided into miR-NC group and miR-129 mimic group. The expression of MAPK3 and p-MAPK3 protein were detected by Western blot and the effect of CDDP treatment on cell apoptosis and proliferation was detected by flow cytometry.

RESULTS

There was a targeted regulation relation between miR-129 and MAPK3 mRNA. MiR-129 expression in MGC-803/CDDP cells was significantly lower than that in MGC-803 cells and the expression of MAPK3 mRNA and protein was significantly higher than that in MGC-803 cells. Compared with miR-NC group, the expression of MAPK3 and p-MAPK3 in MHC-803/CDDP cells in miR-129 mimic transfection group was significantly decreased, with increased cell apoptosis and reduced cell proliferation.

CONCLUSIONS

The decreased expression of miR-129 and the up-regulation of MAPK3 are associated with CDDP resistance in gastric cancer cells. Overexpression of miR-129 inhibits MAPK3 expression and cell proliferation, it induces cell apoptosis and reduces CDDP resistance.

Structures of Endocrine-Disrupting Chemicals Determine Binding to and Activation of the Estrogen Receptor α and Androgen Receptor

Endocrine-disrupting chemicals (EDCs) can interact with nuclear receptors, including estrogen receptor α (ERα) and androgen receptor (AR), to affect the normal endocrine system function, causing severe symptoms. Limited studies queried the EDC mechanisms, focusing on limited chemicals or a set of structurally similar compounds. It remained uncertain how hundreds of diverse EDCs could bind to ERα and AR and cause distinct functional consequences. Here, we employed a series of computational methodologies to investigate the structural features of EDCs that bind to and activate ERα and AR based on more than 4000 compounds. We used molecular docking and molecular dynamics simulations to elucidate the functional consequences and validated structure-function correlations experimentally using a time-resolved fluorescence resonance energy-transfer assay. We found that EDCs share three levels of key fragments. Primary (20 for ERα and 18 for AR) and secondary fragments (38 for ERα and 29 for AR) are responsible for the binding to receptors, and tertiary fragments determine the activity type (agonist, antagonist, or mixed). In summary, our study provides a general mechanism for the EDC function. Discovering the three levels of key fragments may drive fast screening and evaluation of potential EDCs from large sets of commercially used synthetic compounds.

Discriminant models on mitochondrial toxicity improved by consensus modeling and resolving imbalance in training

Humans and animals may be exposed to tens of thousands of natural and synthetic chemicals during their lifespan. It is difficult to assess risk for all the chemicals with experimental toxicity tests. An alternative approach is to use computational toxicology methods such as quantitative structure-activity relationship (QSAR) modeling. Mitochondrial toxicity is involved in many diseases such as cancer, neurodegeneration, type 2 diabetes, cardiovascular diseases and autoimmune diseases. Thus, it is important to rapidly and efficiently identify chemicals with mitochondrial toxicity. In this study, five machine learning algorithms and twelve types of molecular fingerprints were employed to generate QSAR discriminant models for mitochondrial toxicity. A threshold moving method was adopted to resolve the imbalance issue in the training data. Consensus of the models by an averaging probability strategy improved prediction performance. The best model has correct classification rates of 81.8% and 88.3% in ten-fold cross validation and external validation, respectively. Substructures such as phenol, carboxylic acid, nitro and arylchloride were found informative through analysis of information gain and frequency of substructures. The results demonstrate that resolving imbalance in training and building consensus models can improve classification rates for mitochondrial toxicity prediction.

Quantitative Structure-Activity Relationship Models for Predicting Inflammatory Potential of Metal Oxide Nanoparticles

BACKGROUND

Although substantial concerns about the inflammatory effects of engineered nanomaterial (ENM) have been raised, experimentally assessing toxicity of various ENMs is challenging and time-consuming. Alternatively, quantitative structure-activity relationship (QSAR) models have been employed to assess nanosafety. However, no previous attempt has been made to predict the inflammatory potential of ENMs.

OBJECTIVES

By employing metal oxide nanoparticles (MeONPs) as a model ENM, we aimed to develop QSAR models for prediction of the inflammatory potential by their physicochemical properties.

METHODS

We built a comprehensive data set of 30 MeONPs to screen a proinflammatory cytokine interleukin (IL)-1 beta (IL- 1 β ) release in THP-1 cell line. The in vitro hazard ranking was validated in mouse lungs by oropharyngeal instillation of six randomly selected MeONPs. We established QSAR models for prediction of MeONP-induced inflammatory potential via machine learning. The models were further validated against seven new MeONPs. Density functional theory (DFT) computations were exploited to decipher the key mechanisms driving inflammatory responses of MeONPs.

RESULTS

Seventeen out of 30 MeONPs induced excess IL- 1 β production in THP-1 cells. In vivo disease outcomes were highly relevant to the in vitro data. QSAR models were developed for inflammatory potential, with predictive accuracy (ACC) exceeding 90%. The models were further validated experimentally against seven independent MeONPs (ACC = 86 % ). DFT computations and experimental results further revealed the underlying mechanisms: MeONPs with metal electronegativity lower than 1.55 and positive ζ -potential were more likely to cause lysosomal damage and inflammation.

CONCLUSIONS

IL- 1 β released in THP-1 cells can be an index to rank the inflammatory potential of MeONPs. QSAR models based on IL- 1 β were able to predict the inflammatory potential of MeONPs. Our approach overcame the challenge of time- and labor-consuming biological experiments and allowed for computational assessment of MeONP inflammatory potential by characterization of their physicochemical properties. https://doi.org/10.1289/EHP6508.

Association of FLAIR vascular hyperintensity and acute MCA stroke outcome changes with the severity of leukoaraiosis

PURPOSE

The clinical significance of FLAIR vascular hyperintensity (FVH), a marker of collateral circulation in ischaemic stroke, remains controversial. We hypothesised that the association between FVH and outcomes varies with the severity of leukoaraiosis (LA), another marker of collaterals, and that their combined significance may vary with time.

METHODS

We included 459 consecutive patients with middle cerebral artery (MCA) stroke. Proximal and distal FVHs were distinguished based on location. LA was divided into two grades, according to Fazekas scores of 0-2 and 3-6. Symptom-to-MRI time was divided into two categories: ≤ 14 days and ≥ 15 days.

RESULTS

We found no difference in FVH proportion according to LA grade. Multivariate analysis revealed that LA and FVH status were independently associated with unfavourable outcomes (modified Rankin scale ≥ 2) in patients with symptom-to-MRI times ≤ 14 days (P = 0.008), but not in those with symptom-to-MRI times ≥15 days (P = 0.61). In the group with symptom-to-MRI times ≤14 days, patients with LA 3-6 and FVH(+) (OR, 3.044; 95% CI, 1.116-8.305) were more likely to have unfavourable clinical outcomes compared with patients with LA 0-2 and FVH(+) but not compared with those with LA 0-2 and FVH(-) or LA 3-6 and FVH(-). In addition, FVH location did not influence the relationship between FVH and outcomes.

CONCLUSIONS

The association between FVH and outcomes was influenced by the degree of LA in the acute but not in the subacute and chronic stages of MCA infarction. FVH predicts clinical outcomes independently only in those with more extensive LA.

Development of a Nicotinic Acetylcholine Receptor nAChR α7 Binding Activity Prediction Model

Despite the well-known adverse health effects associated with tobacco use, addiction to nicotine found in tobacco products causes difficulty in quitting among users. Nicotinic acetylcholine receptors (nAChRs) are the physiological targets of nicotine and facilitate addiction to tobacco products. The nAChR-α7 subtype plays an important role in addiction; therefore, predicting the binding activity of tobacco constituents to nAChR-α7 is an important component for assessing addictive potential of tobacco constituents. We developed an α7 binding activity prediction model based on a large training data set of 843 chemicals with human α7 binding activity data extracted from PubChem and ChEMBL. The model was tested using 1215 chemicals with rat α7 binding activity data from the same databases. Based on the competitive docking results, the docking scores were partitioned to the key residues that play important roles in the receptor-ligand binding. A decision forest was used to train the human α7 binding activity prediction model based on the partition of docking scores. Five-fold cross validations were conducted to estimate the performance of the decision forest models. The developed model was used to predict the potential human α7 binding activity for 5275 tobacco constituents. The human α7 binding activity data for 84 of the 5275 tobacco constituents were experimentally measured to confirm and empirically validate the prediction results. The prediction accuracy, sensitivity, and specificity were 64.3, 40.0, and 81.6%, respectively. The developed prediction model of human α7 may be a useful tool for high-throughput screening of potential addictive tobacco constituents.

[Relations between high risk sexual behavior and HIV infection among men who have sex with men in ways of meeting male partners]

Objective: To understand the relations between high risk sexual behavior and HIV infection among MSM in ways of finding male partners in Ningbo. Methods: A cross-sectional study was conducted in Ningbo between April and November in 2018. Data related to socio-demographics, ways of finding male partners, adoption of gay apps and sexual behaviors were collected by snowball method. Blood samples were drawn for HIV antibody testing. Classified data was evaluated by chi-square test. Related factors on HIV infection were analyzed by multivariate logistic regression. Results: A total of 735 participants were included in this study. Ways of finding male partners would through gay apps (60.8%, 447/735), QQ/Wechat (32.3%, 237/735) and gay-places (6.9%, 51/735). Related information on high risk sexual behavior and HIV infection among gay apps users were found as: 16.8%(75) had sexual behavior once per week in the past 6 months, 41.8% (187/447) had multiple sexual partners, 12.1% (54/447) had unprotected anal intercourse in the last time, 52.3% (234/447) having had unprotected anal intercourse in the past 6 months. The overall HIV prevalence rate was 12.1%(54/447). Among the HIV cases who got infected within the two years, 68.6%(24/35) of them had used gay apps for less than two years. Results from the, multivariate logistic regression analysis showed that gay apps users were more susceptible to infected HIV than those who used the QQ/Wechat (OR=3.03, 95%CI: 1.30-7.07). Conclusions: Gay apps was popularly known among the Ningbo MSM, and was associated with the high risk sexual behaviors and HIV infection. HIV control and prevention programs should be strengthened in the MSM population who used the gay apps. Related surveillance and intervention programs for MSM, who use the gay apps, need to be further reinforced.

CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity

BACKGROUND

Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling.

OBJECTIVES

In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP).

METHODS

The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast™ metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast™/Tox21 HTS in vitro assays.

RESULTS

The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set.

DISCUSSION

The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of ∼875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment. https://doi.org/10.1289/EHP5580.

CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity

BACKGROUND

Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling.

OBJECTIVES

In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP).

METHODS

The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast™ metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast™/Tox21 HTS in vitro assays.

RESULTS

The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set.

DISCUSSION

The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of ∼ 875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment. https://doi.org/10.1289/EHP5580.

547 Point-of-care cardiac assessment using machine learning to guide image acquisition

Funding Acknowledgements

Bay Labs, Inc; San Francisco, CA

Background/Introduction: When used by experienced examiners, the utility of point-of-care (POC) ultrasound for assessing cardiac anatomy and function has been well established. However, in some clinical circumstances (Primary Care offices, Intensive Care Unit, some Emergency Rooms, or in remote settings) in which a rapid assessment of cardiac anatomy and dynamics can facilitate patient care, an examiner experienced at POC scanning may not be immediately available.

Purpose

To help novice users acquire clinically useful standard cardiac views using novel machine learning (ML) software.

Methods

We used an investigational device that employs ML software to provide real-time adaptive guidance of transducer position and orientation to help novice users acquire tomographic views of the heart. We tested the utility of this approach when 4 nurses with no prior training in sonography performed POC studies on 16 subjects (10 healthy, 6 with cardiac abnormalities; 9 men; body mass index normal in 6, overweight in 6, and obese in 4 subjects). Each nurse underwent didactic training and 4 hours of supervised practice using the ML program. Each nurse scanned each study subject using a scanner equipped with ML software to acquire 10 digital two-dimensional image clips, including: parasternal long axis, short axis at the aortic valve, mitral valve, and mid-left ventricle (LV), apical 2-, 4-, and 5-chamber, subcostal 4-chamber, and longitudinal views of the inferior vena cava (IVC). All video clips (n = 640) were later reviewed independently by 5 level 3-trained cardiologists who were blinded to subject, scanner, and each other"s assessments. The expert readers reviewed each set of 10 clips to determine if the following variables could be assessed qualitatively: LV size and function; right ventricular (RV) size and function; aortic, mitral and tricuspid valves; pericardial effusion; left atrial size; IVC size.

Results

The majority of expert readers concurred, independently, that the sets of images acquired by nurses using ML guidance allowed qualitative assessment of LV size and function in 98%, pericardial effusion in 98%, RV size and function in 92%, and aortic and mitral valve anatomy and dynamics in 94-97% of cases. Qualitative assessment of LA size was feasible in 95%. Images of the IVC were judged as adequate for assessment in 58%.

Conclusion

This preliminary study suggests the potential value of novel ML software by demonstrating that nurses with limited training can acquire tomographic images useful for qualitative assessment of the cardiac chambers and valves in more than 90% of the subjects examined. This approach might be useful when timely POC cardiac assessment is indicated in settings where an experienced examiner is not available. Further refinements in the guiding software are needed to improve the success rate of IVC imaging, since IVC size can be a useful indicator of volume status.

Persistent Organic Pollutants in Food: Contamination Sources, Health Effects and Detection Methods

Persistent organic pollutants (POPs) present in foods have been a major concern for food safety due to their persistence and toxic effects. To ensure food safety and protect human health from POPs, it is critical to achieve a better understanding of POP pathways into food and develop strategies to reduce human exposure. POPs could present in food in the raw stages, transferred from the environment or artificially introduced during food preparation steps. Exposure to these pollutants may cause various health problems such as endocrine disruption, cardiovascular diseases, cancers, diabetes, birth defects, and dysfunctional immune and reproductive systems. This review describes potential sources of POP food contamination, analytical approaches to measure POP levels in food and efforts to control food contamination with POPs.

Outcomes of Systemic Lupus Erythematosus in Patients who Discontinue Hydroxychloroquine

Background

Hydroxychloroquine (HCQ) is an antimalarial drug that is recommended as a safe, daily prophylactic intervention for individuals with systemic lupus erythematosus (SLE) based on previous studies that showed an association of HCQ use with reductions in flares compared with placebo. Our study aims to determine whether the discontinuation of HCQ leads to relapse of disease and whether the duration of HCQ use impacts the success of its eventual discontinuation.

Methods

A retrospective chart review was performed on the medical records of patients diagnosed with SLE between July 1, 2006, and June 30, 2016. The data gathered included demographic factors, diagnostic symptoms, laboratory values, and SLE medications. Additionally, HCQ usage and discontinuation rates were collected as well as the timing and prevalence of flares during and after HCQ usage. Patients who were diagnosed with SLE but never used HCQ were excluded from the study. The occurrence of flares, clinical characteristics, and duration of treatment with HCQ were compared between the group that continued HCQ and the group that discontinued HCQ.

Results

Of the 509 patients who met inclusion criteria, 66.2% (n = 337) continued HCQ throughout the duration of their treatment (median duration of HCQ treatment was 8.0 years), whereas 33.8% (n = 172) did not (median duration of HCQ treatment was 1.9 years). Patients who received HCQ for less than 1 year before discontinuation (median duration of HCQ treatment was 2.5 months) were more likely to experience SLE flares compared with those who continued HCQ for more than 1 year (13.1% vs 5.7%, P = 0.019). Patients who experienced a flare while on HCQ were more likely to have arthritis, oral ulcers, leukopenia, and thrombocytopenia.

Conclusion

With over 500 patient charts reviewed, this is the largest study comparing outcomes for patients on HCQ with those who discontinued it. Patients who discontinue HCQ after being on it for less than 1 year are at greater risk for flares compared with those who take HCQ for longer than 1 year. These findings should be used to guide treatment, educate patients on the role of continued treatment with HCQ, and ultimately reduce morbidity and mortality.

P4347Automated echocardiographic quantification of left ventricular ejection fraction without volume measurements using a machine learning algorithm mimicking a human expert

Background

Echocardiographic quantification of left ventricular (LV) ejection fraction (EF) relies on either manual or automated identification of endocardial boundaries followed by standard calculation of model-based end-systolic and end-diastolic LV volumes. Recent developments in artificial intelligence resulted in computer algorithms that allow near automated detection of endocardial boundaries and measurement of LV volumes and function. However, boundary identification is still prone to errors limiting accuracy in certain patients. We hypothesized that a fully automated machine learning algorithm could be developed, which circumvents border detection and instead estimates the degree of ventricular contraction, similar to a human expert trained on tens of thousands of images.

Purpose

This study was designed to test the feasibility and accuracy of this approach.

Methods

Machine learning algorithm was developed and trained on a database of >50,000 echocardiographic studies, including multiple apical 2- and 4-chamber views, to automatically estimate LVEF (AutoEF, BayLabs). Testing was performed on an independent group of 99 unselected patients, whose automated EF values were compared to reference values obtained by averaging measurements by 3 experts using conventional volume-based technique. Inter-technique agreement was assessed using linear regression and Bland-Altman analysis of bias and limits of agreement (LOA). Consistency was assessed by mean absolute deviation (MAD) among automated estimates based on different combinations of apical views. Finally, sensitivity and specificity of detecting of EF≤35% was calculated. These metrics were compared side-by-side against the same reference standard to those obtained from conventional EF measurements by clinical readers.

Results

Automated estimation of LVEF was feasible in all 99 patients. AutoEF values showed high consistency (MAD=2.9%) and excellent agreement with the reference values: r=0.95, bias=1.0%, LOA=±11.8%, with sensitivity 0.90 and specificity 0.92 for detection of EF≤35%. This was similar to clinicians' measurements: r=0.94, bias=1.4%, LOA=±13.4%,sensitivity 0.93, specificity 0.87.

Conclusions

Machine learning algorithm for volume-independent LVEF estimation is highly feasible and similar in accuracy to conventional volume-based measurements, when compared to reference values provided by an expert panel.

Acknowledgement/Funding

Bay Labs, Inc.

Evaluation of miLINC to shorten time to treatment for rifampicin-resistant Mycobacterium tuberculosis

BACKGROUND: Achieving the 90-90-90 targets for tuberculosis (TB) will require interventions that enhance diagnosis, linkage, treatment and adherence to care. As a first step in the process, our team designed a suite of smartphone applications known as miLINC to improve time from diagnosis to treatment initiation in drug-resistant TB patients.SETTING: Three clinical locations in a large, peri-urban district in KwaZulu-Natal, South Africa.OBJECTIVE: To assess the acceptability, feasibility and impact of the miLINC mobile health applications as a solution to reducing the time from presentation to treatment initiation of rifampicin-resistant (RR) TB patients.METHODS: We used a prospective, observational quality improvement evaluation of miLINC's impact among newly diagnosed patients with RR-TB.RESULTS: A convenience sample comprising details of 6341 patients with presumptive TB were entered into miLINC. Of the 631 TB-positive sputum specimens, 41 (6.5%) were found to be RR-TB. The mean time from clinical presentation to RR-TB treatment initiation was 3 days, 21 h, 17 min.CONCLUSION: This is the first study to suggest that the time from presentation to diagnosis and to treatment initiation for patients with RR-TB can be significantly improved using an integrated approach combining technology with appropriate human resources.

The Association between FLAIR Vascular Hyperintensity and Stroke Outcome Varies with Time from Onset

BACKGROUND AND PURPOSE

FLAIR vascular hyperintensity has been recognized as a marker of collaterals in ischemic stroke, but the impact on outcome is still controversial. We hypothesized that the association between FLAIR vascular hyperintensity and outcome varies with time.

MATERIALS AND METHODS

We included 459 consecutive patients with middle cerebral artery stroke and divided them into 3 groups by symptom-to-MR imaging time (group 1, ≤7 days; group 2, 8-14 days; group 3, ≥15 days). The FLAIR vascular hyperintensity score, ranging from 0 to 3 points, was based on territory distributions of different MCA segments. The associations between FLAIR vascular hyperintensity and outcome with time were analyzed qualitatively and quantitatively.

RESULTS

No patients underwent MR imaging within 6 hours of onset. The proportion of FLAIR vascular hyperintensity (+) and severe stenosis or occlusion of MCA was not significantly dependent on time. In groups 1 and 2, FLAIR vascular hyperintensity (+) was significantly associated with larger lesions, the prevalence of flow injury, and unfavorable outcome (mRS ≥ 2). There were no such associations in group 3. Multiple logistic regressions demonstrated that FLAIR vascular hyperintensity (+) was an independent risk factor for unfavorable outcome in group 2. Infarction volume tended to increase with the increase of the distal FLAIR vascular hyperintensity score in groups 1 and 2, while declining in group 3.

CONCLUSIONS

FLAIR vascular hyperintensity is associated with unfavorable outcome within 6 hours to 14 days of onset, while the wider distribution of distal FLAIR vascular hyperintensity may be favorable beyond 14 days of onset in MCA infarction. Symptom-to-MR imaging time should be considered when assessing the prognostic value of FLAIR vascular hyperintensity.

Increased risk of aminoglycoside-induced hearing loss in MDR-TB patients with HIV coinfection

SETTING

A high proportion of individuals with multidrug-resistant tuberculosis (MDR-TB) develop permanent hearing loss due to ototoxicity caused by injectable aminoglycosides (AGs). The prevalence of AG-induced hearing loss is greatest in tuberculosis (TB) and human immunodeficiency virus (HIV) endemic countries in sub-Saharan Africa. However, whether HIV coinfection is associated with a higher incidence of AG-induced hearing loss during MDR-TB treatment is controversial.

OBJECTIVE

To evaluate the impact of HIV coinfection on AG-induced hearing loss among individuals with MDR-TB in sub-Saharan Africa.

DESIGN

This was a meta-analysis of articles published in PubMed, Embase, Scopus, Cumulative Index to Nursing and Allied Health Literature, Web of Science, Cochrane Review, and reference lists using search terms 'hearing loss', 'aminoglycoside', and 'sub-Saharan Africa'.

RESULTS

Eight studies conducted in South Africa, Botswana and Namibia and published between 2012 and 2016 were included. As the included studies were homogeneous (χ2 = 8.84, df = 7), a fixed-effects model was used. Individuals with MDR-TB and HIV coinfection had a 22% higher risk of developing AG-induced hearing loss than non-HIV-infected individuals (pooled relative risk 1.22, 95%CI 1.10-1.36) during MDR-TB treatment.

CONCLUSION

This finding is critical for TB programs with regard to the expansion of injectable-sparing regimens. Our findings lend credibility to using injectable-sparing regimens and more frequent hearing monitoring, particularly in resource-limited settings for HIV-coinfected individuals.