XPG gene polymorphisms and glioma susceptibility: a two-centre case-control study

Background: Glioma, the most common tumour in children next to leukaemia, is difficult to treat, with a poor prognosis and high recurrence rate. Xeroderma pigmentosum group G (XPG) plays a key role in the nucleotide excision repair pathway, which may modulate individual susceptibility to developing cancer. We hypothesized links between XPG variants and glioma in children.Methods: We tested our hypothesis in a study comparing 171 glioma cases with 228 age and sex matched controls, determining XPG polymorphisms rs2094258 C > T, rs751402 C > T, rs2296147 T > C, rs1047768 T > C, rs873601 G > A by standard molecular genetic methods.Results: rs2094258 C > T was associated with a decreased glioma risk, but carrying the rs1047768 C or rs873601 A allele brought an increased risk. Subjects carrying 5 risk genotypes had a significantly increased glioma risk at an adjusted odds ratio of 1.97 (95% confidence Interval 1.26-3.08)(p = 0.003) when compared with those carrying 0-4 risk genotypes. Furthermore, children with 5 risk genotypes had a higher glioma risk when aged >60 months, were more likely to be male, and with subtypes of astrocytic tumours, and low-grade clinical stage, when compared to those with 0-4 risk genotypes. Preliminary functional exploration suggested that rs2094258 is linked with the expression of its surrounding genes in the expression quantitative trait locus analysis.Conclusion: Certain variants of XPG are risk factors for paediatric glioma, and so may be useful in early diagnosis.

Pericoronary adipose tissue attenuation (PCAT) is associated with high-risk atherosclerotic plaque and subsequent acute coronary syndrome in patients with stable coronary artery disease

Background

High-risk plaque (HRP) features detected on computed tomography coronary angiography (CTCA) are associated with subsequent development of acute coronary syndrome (ACS). Pericoronary adipose tissue attenuation (PCAT) is a novel CT-derived non-invasive marker of coronary inflammation. The association between PCAT, HRP and outcomes is unknown.

Purpose

To evaluate the association between high-risk plaque HRP characteristics, ACS and PCAT attenuation as a surrogate marker of coronary inflammation.

Methods

Patients with stable chest pain referred for CTCA between 2011 and 2013 with outcome data were included. Patients with HRP were propensity matched to patients without HRP. PCAT-RCA and PCAT-lesion were assessed using semi-automated software (AutoPlaque version 2.0). PCAT-RCA was measured around the proximal 10–50 mm of the right coronary artery (RCA). PCAT-lesion was measured around the HRP or lesion with highest-grade stenosis in patients without HRP.

Results

78 patients (67±10 years, 67% males) included 41 (52.6%) patients with HRP. Median PCAT-RCA was higher in HRP patients than non-HRP patients (−80.4 HU [IQR −86.1, −76.5] vs. −86.3 HU [IQR −92.2, −77.0], p=0.02). PCAT-Lesion was not significantly different between HRP and non-HRP patients (−78.7 HU [IQR −84.4, −73.4] vs. −82.8 HU [IQR −91.1, −75.4], p=0.16). More patients with HRP developed a subsequent ACS (24.4% vs. 5.4%, p=0.008). HRP patients who experienced an ACS demonstrated a higher PCAT-RCA (−76.6 HU [IQR −81.3, −72.4] vs. −82.6 HU [IQR −87.4, −78.0], p=0.018) and PCAT-lesion (−74.3 HU [IQR −77.1, −68.4] vs. −80.7 HU [IQR −84.8, −74.9], p=0.04) compared to those who did not experience an ACS.

Conclusion

PCAT attenuation is elevated in stable CAD patients with HRP features. Measures of periarterial inflammation were also more evident in HRP patients who develop ACS, indicating a potential contribution to vulnerability. The implications for integration of these measures in clinical practice require further investigation.

Funding Acknowledgement

Type of funding source: None

Characterization of MR Imaging-Visible Perivascular Spaces in the White Matter of Healthy Adolescents at 3T

BACKGROUND AND PURPOSE

Perivascular spaces play a role in cerebral waste removal and neuroinflammation. Our aim was to provide data regarding the burden of MR imaging-visible perivascular spaces in white matter in healthy adolescents using an automated segmentation method and to establish relationships between common demographic characteristics and perivascular space burden.

MATERIALS AND METHODS

One hundred eighteen 12- to 21-year-old subjects underwent T1- and T2-weighted 3T MR imaging as part of the National Consortium on Alcohol and Neurodevelopment in Adolescence. Perivascular spaces were identified in WM on T2-weighted imaging using a local heterogeneity approach coupled with morphologic constraints, and their spatial distribution and geometric characteristics were assessed.

RESULTS

MR imaging-visible perivascular spaces were identified in all subjects (range, 16-287). Males had a significantly higher number of perivascular spaces than females: males, mean, 98.4 ± 50.5, versus females, 70.7 ± 36.1, (P < .01). Perivascular space burden was bilaterally symmetric (r > 0.4, P < .01), and perivascular spaces were more common in the frontal and parietal lobes than in the temporal and occipital lobes (P < .01). Age and pubertal status were not significantly associated with perivascular space burden.

CONCLUSIONS

Despite a wide range of burden, perivascular spaces are present in all healthy adolescents. Perivascular space burden is higher in adolescent males than in females, regardless of age and pubertal status. In this population, perivascular spaces are highly symmetric. Although widely reported as a feature of the aging brain, awareness of the presence of perivascular spaces in a cohort of healthy adolescents provides the foundation for further research regarding the role of these structural variants in health and disease.

Characterization of on-target adverse events caused by TRK inhibitor therapy

BACKGROUND

The tropomyosin receptor kinase (TRK) pathway controls appetite, balance, and pain sensitivity. While these functions are reflected in the on-target adverse events (AEs) observed with TRK inhibition, these AEs remain under-recognized, and pain upon drug withdrawal has not previously been reported. As TRK inhibitors are approved by multiple regulatory agencies for TRK or ROS1 fusion-positive cancers, characterizing these AEs and corresponding management strategies is crucial.

PATIENTS AND METHODS

Patients with advanced or unresectable solid tumors treated with a TRK inhibitor were retrospectively identified in a search of clinical databases. Among these patients, the frequency, severity, duration, and management outcomes of AEs including weight gain, dizziness or ataxia, and withdrawal pain were characterized.

RESULTS

Ninety-six patients with 15 unique cancer histologies treated with a TRK inhibitor were identified. Weight gain was observed in 53% [95% confidence interval (CI), 43%-62%] of patients and increased with time on TRK inhibition. Pharmacologic intervention, most commonly with glucagon-like peptide 1 analogs or metformin, appeared to result in stabilization or loss of weight. Dizziness, with or without ataxia, was observed in 41% (95% CI, 31%-51%) of patients with a median time to onset of 2 weeks (range, 3 days to 16 months). TRK inhibitor dose reduction was the most effective intervention for dizziness. Pain upon temporary or permanent TRK inhibitor discontinuation was observed in 35% (95% CI, 24%-46%) of patients; this was more common with longer TRK inhibitor use. TRK inhibitor reinitiation was the most effective intervention for withdrawal pain.

CONCLUSIONS

TRK inhibition-related AEs including weight gain, dizziness, and withdrawal pain occur in a substantial proportion of patients receiving TRK inhibitors. This safety profile is unique relative to other anticancer therapies and warrants careful monitoring. These on-target toxicities are manageable with pharmacologic intervention and dose modification.

Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study

Background

Successful integrations of machine learning into routine clinical care are exceedingly rare, and barriers to its adoption are poorly characterized in the literature.

Objective

This study aims to report a quality improvement effort to integrate a deep learning sepsis detection and management platform, Sepsis Watch, into routine clinical care.

Methods

In 2016, a multidisciplinary team consisting of statisticians, data scientists, data engineers, and clinicians was assembled by the leadership of an academic health system to radically improve the detection and treatment of sepsis. This report of the quality improvement effort follows the learning health system framework to describe the problem assessment, design, development, implementation, and evaluation plan of Sepsis Watch.

Results

Sepsis Watch was successfully integrated into routine clinical care and reshaped how local machine learning projects are executed. Frontline clinical staff were highly engaged in the design and development of the workflow, machine learning model, and application. Novel machine learning methods were developed to detect sepsis early, and implementation of the model required robust infrastructure. Significant investment was required to align stakeholders, develop trusting relationships, define roles and responsibilities, and to train frontline staff, leading to the establishment of 3 partnerships with internal and external research groups to evaluate Sepsis Watch.

Conclusions

Machine learning models are commonly developed to enhance clinical decision making, but successful integrations of machine learning into routine clinical care are rare. Although there is no playbook for integrating deep learning into clinical care, learnings from the Sepsis Watch integration can inform efforts to develop machine learning technologies at other health care delivery systems.

Machine learning for early detection of sepsis: an internal and temporal validation study

OBJECTIVE

Determine if deep learning detects sepsis earlier and more accurately than other models. To evaluate model performance using implementation-oriented metrics that simulate clinical practice.

MATERIALS AND METHODS

We trained internally and temporally validated a deep learning model (multi-output Gaussian process and recurrent neural network [MGP-RNN]) to detect sepsis using encounters from adult hospitalized patients at a large tertiary academic center. Sepsis was defined as the presence of 2 or more systemic inflammatory response syndrome (SIRS) criteria, a blood culture order, and at least one element of end-organ failure. The training dataset included demographics, comorbidities, vital signs, medication administrations, and labs from October 1, 2014 to December 1, 2015, while the temporal validation dataset was from March 1, 2018 to August 31, 2018. Comparisons were made to 3 machine learning methods, random forest (RF), Cox regression (CR), and penalized logistic regression (PLR), and 3 clinical scores used to detect sepsis, SIRS, quick Sequential Organ Failure Assessment (qSOFA), and National Early Warning Score (NEWS). Traditional discrimination statistics such as the C-statistic as well as metrics aligned with operational implementation were assessed.

RESULTS

The training set and internal validation included 42 979 encounters, while the temporal validation set included 39 786 encounters. The C-statistic for predicting sepsis within 4 h of onset was 0.88 for the MGP-RNN compared to 0.836 for RF, 0.849 for CR, 0.822 for PLR, 0.756 for SIRS, 0.619 for NEWS, and 0.481 for qSOFA. MGP-RNN detected sepsis a median of 5 h in advance. Temporal validation assessment continued to show the MGP-RNN outperform all 7 clinical risk score and machine learning comparisons.

CONCLUSIONS

We developed and validated a novel deep learning model to detect sepsis. Using our data elements and feature set, our modeling approach outperformed other machine learning methods and clinical scores.

A hexavalent Coxsackievirus B vaccine is highly immunogenic and has a strong protective capacity in mice and nonhuman primates

Coxsackievirus B (CVB) enteroviruses are common human pathogens known to cause severe diseases including myocarditis, chronic dilated cardiomyopathy, and aseptic meningitis. CVBs are also hypothesized to be a causal factor in type 1 diabetes. Vaccines against CVBs are not currently available, and here we describe the generation and preclinical testing of a novel hexavalent vaccine targeting the six known CVB serotypes. We show that the vaccine has an excellent safety profile in murine models and nonhuman primates and that it induces strong neutralizing antibody responses to the six serotypes in both species without an adjuvant. We also demonstrate that the vaccine provides immunity against acute CVB infections in mice, including CVB infections known to cause virus-induced myocarditis. In addition, it blocks CVB-induced diabetes in a genetically permissive mouse model. Our preclinical proof-of-concept studies demonstrate the successful generation of a promising hexavalent CVB vaccine with high immunogenicity capable of preventing CVB-induced diseases.