Techniques for Arbuscular Mycorrhiza Inoculum Reduction

It is well established that arbuscular mycorrhizal (AM) fungi can play a significant role in sustainable crop production and environmental conservation. With the increasing awareness of the ecological significance of mycorrhizas and their diversity, research needs to be directed away from simple records of their occurrence or casual speculation of their function (Smith and Read 1997). Rather, the need is for empirical studies and investigations of the quantitative aspects of the distribution of different types and their contribution to the function of ecosystems. There is no such thing as a fungal effect or a plant effect, but there is an interaction between both symbionts. This results from the AM fungi and plant community size and structure, soil and climatic conditions, and the interplay between all these factors (Kahiluoto et al. 2000). Consequently, it is readily understood that it is the problems associated with methodology that limit our understanding of the functioning and effects of AM fungi within field communities. Given the ubiquous presence of AM fungi, a major constraint to the evaluation of the activity of AM colonisation has been the need to account for the indigenous soil native inoculum. This has to be controlled (i.e. reduced or eliminated) if we are to obtain a true control treatment for analysis of arbuscular mycorrhizas in natural substrates. There are various procedures possible for achieving such an objective, and the purpose of this chapter is to provide details of a number of techniques and present some evaluation of their advantages and disadvantages. Although there have been a large number of experiments to investigated the effectiveness of different sterilization procedures for reducing pathogenic soil fungi, little information is available on their impact on beneficial organisms such as AM fungi. Furthermore, some of the techniques have been shown to affect physical and chemical soil characteristics as well as eliminate soil microorganisms that can interfere with the development of mycorrhizas, and this creates difficulties in the interpretation of results simply in terms of possible mycorrhizal activity. An important subject is the differentiation of methods that involve sterilization from those focussed on indigenous inoculum reduction. Soil sterilization aims to destroy or eliminate microbial cells while maintaining the existing chemical and physical characteristics of the soil (Wolf and Skipper 1994). Consequently, it is often used for experiments focussed on specific AM fungi, or to establish a negative control in some other types of study. In contrast, the purpose of inoculum reduction techniques is to create a perturbation that will interfere with mycorrhizal formation, although not necessarily eliminating any component group within the inoculum. Such an approach allows the establishment of different degrees of mycorrhizal formation between treatments and the study of relative effects. Frequently the basic techniques used to achieve complete sterilization or just an inoculum reduction may be similar but the desired outcome is accomplished by adjustments of the dosage or intensity of the treatment. The ultimate choice of methodology for establishing an adequate non-mycorrhizal control depends on the design of the particular experiments, the facilities available and the amount of soil requiring treatment

Deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction: an international multicentre study

BACKGROUND: Atherosclerotic plaque quantification from coronary CT angiography (CCTA) enables accurate assessment of coronary artery disease burden and prognosis. We sought to develop and validate a deep learning system for CCTA-derived measures of plaque volume and stenosis severity. METHODS: This international, multicentre study included nine cohorts of patients undergoing CCTA at 11 sites, who were assigned into training and test sets. Data were retrospectively collected on patients with a wide range of clinical presentations of coronary artery disease who underwent CCTA between Nov 18, 2010, and Jan 25, 2019. A novel deep learning convolutional neural network was trained to segment coronary plaque in 921 patients (5045 lesions). The deep learning network was then applied to an independent test set, which included an external validation cohort of 175 patients (1081 lesions) and 50 patients (84 lesions) assessed by intravascular ultrasound within 1 month of CCTA. We evaluated the prognostic value of deep learning-based plaque measurements for fatal or non-fatal myocardial infarction (our primary outcome) in 1611 patients from the prospective SCOT-HEART trial, assessed as dichotomous variables using multivariable Cox regression analysis, with adjustment for the ASSIGN clinical risk score. FINDINGS: In the overall test set, there was excellent or good agreement, respectively, between deep learning and expert reader measurements of total plaque volume (intraclass correlation coefficient [ICC] 0·964) and percent diameter stenosis (ICC 0·879; both p<0·0001). When compared with intravascular ultrasound, there was excellent agreement for deep learning total plaque volume (ICC 0·949) and minimal luminal area (ICC 0·904). The mean per-patient deep learning plaque analysis time was 5·65 s (SD 1·87) versus 25·66 min (6·79) taken by experts. Over a median follow-up of 4·7 years (IQR 4·0–5·7), myocardial infarction occurred in 41 (2·5%) of 1611 patients from the SCOT-HEART trial. A deep learning-based total plaque volume of 238·5 mm(3) or higher was associated with an increased risk of myocardial infarction (hazard ratio [HR] 5·36, 95% CI 1·70–16·86; p=0·0042) after adjustment for the presence of deep learning-based obstructive stenosis (HR 2·49, 1·07–5·50; p=0·0089) and the ASSIGN clinical risk score (HR 1·01, 0·99–1·04; p=0·35). INTERPRETATION: Our novel, externally validated deep learning system provides rapid measurements of plaque volume and stenosis severity from CCTA that agree closely with expert readers and intravascular ultrasound, and could have prognostic value for future myocardial infarction

Epicardial fat is associated with duration of antiretroviral therapy and coronary atherosclerosis

ObjectiveCytokines released by epicardial fat are implicated in the pathogenesis of atherosclerosis. HIV infection and antiretroviral therapy have been associated with changes in body fat distribution and coronary artery disease. We sought to determine whether HIV infection is associated with greater epicardial fat and whether epicardial fat is associated with subclinical coronary atherosclerosis.DesignWe studied 579 HIV-infected and 353 HIV-uninfected men aged 40-70 years with noncontrast computed tomography to measure epicardial adipose tissue (EAT) volume and coronary artery calcium (CAC). Total plaque score (TPS) and plaque subtypes (noncalcified, calcified, and mixed) were measured by coronary computed tomography angiography in 706 men.MethodsWe evaluated the association between EAT and HIV serostatus, and the association of EAT with subclinical atherosclerosis, adjusting for age, race, and serostatus and with additional cardiovascular risk factors and tested for modifying effects of HIV serostatus.ResultsHIV-infected men had greater EAT than HIV-uninfected men (P=0.001). EAT was positively associated with duration of antiretroviral therapy (P=0.02), specifically azidothymidine (P&lt;0.05). EAT was associated with presence of any coronary artery plaque (P=0.006) and noncalcified plaque (P=0.001), adjusting for age, race, serostatus, and cardiovascular risk factors. Among men with CAC, EAT was associated with CAC extent (P=0.006). HIV serostatus did not modify associations between EAT and either CAC extent or presence of plaque.ConclusionGreater epicardial fat volume in HIV-infected men and its association with coronary plaque and antiretroviral therapy duration suggest potential mechanisms that might lead to increased risk for cardiovascular disease in HIV

Deep Learning–Based Quantification of Epicardial Adipose Tissue Volume and Attenuation Predicts Major Adverse Cardiovascular Events in Asymptomatic Subjects

BackgroundEpicardial adipose tissue (EAT) volume (cm3) and attenuation (Hounsfield units) may predict major adverse cardiovascular events (MACE). We aimed to evaluate the prognostic value of fully automated deep learning-based EAT volume and attenuation measurements quantified from noncontrast cardiac computed tomography.MethodsOur study included 2068 asymptomatic subjects (56±9 years, 59% male) from the EISNER trial (Early Identification of Subclinical Atherosclerosis by Noninvasive Imaging Research) with long-term follow-up after coronary artery calcium measurement. EAT volume and mean attenuation were quantified using automated deep learning software from noncontrast cardiac computed tomography. MACE was defined as myocardial infarction, late (&gt;180 days) revascularization, and cardiac death. EAT measures were compared to coronary artery calcium score and atherosclerotic cardiovascular disease risk score for MACE prediction.ResultsAt 14±3 years, 223 subjects suffered MACE. Increased EAT volume and decreased EAT attenuation were both independently associated with MACE. Atherosclerotic cardiovascular disease risk score, coronary artery calcium, and EAT volume were associated with increased risk of MACE (hazard ratio [95%CI]: 1.03 [1.01-1.04]; 1.25 [1.19-1.30]; and 1.35 [1.07-1.68], P&lt;0.01 for all) and EAT attenuation was inversely associated with MACE (hazard ratio, 0.83 [95% CI, 0.72-0.96]; P=0.01), with corresponding Harrell C statistic of 0.76. MACE risk progressively increased with EAT volume ≥113 cm3 and coronary artery calcium ≥100 AU and was highest in subjects with both (P&lt;0.02 for all). In 1317 subjects, EAT volume was correlated with inflammatory biomarkers C-reactive protein, myeloperoxidase, and adiponectin reduction; EAT attenuation was inversely related to these biomarkers.ConclusionsFully automated EAT volume and attenuation quantification by deep learning from noncontrast cardiac computed tomography can provide prognostic value for the asymptomatic patient, without additional imaging or physician interaction

Extra-coronary calcification (aortic valve calcification, mitral annular calcification, aortic valve ring calcification and thoracic aortic calcification) in HIV seropositive and seronegative men: Multicenter AIDS Cohort Study.

IntroductionPrevious studies have demonstrated an association between HIV infection and coronary artery disease (CAD); little is known about potential associations between HIV infection and extra-coronary calcification (ECC).MethodsWe analyzed 621 HIV infected (HIV+) and 384 HIV uninfected (HIV-) men from the Multicenter AIDS Cohort Study who underwent non-contrast computed tomography (CT) from 2010-2013. Agatston scores were calculated for mitral annular calcification (MAC), aortic valve calcification (AVC), aortic valve ring calcification (AVRC), and thoracic aortic calcification (TAC). The associations between HIV infection and the presence of each type of ECC (score &gt; 0) were evaluated by multivariable logistic regression. We also evaluated the association of ECC with inflammatory biomarker levels and coronary plaque morphology.ResultsAmong HIV+ and HIV- men, the age-standardized prevalences were 15% for TAC (HIV+ 14%/HIV- 16%), 10% for AVC (HIV+ 11%/HIV- 8%), 24% for AVRC (HIV+ 23% HIV- 24%), and 5% for MAC (HIV+ 7%/HIV- 3%). After adjustment, HIV+ men had 3-fold greater odds of MAC compared to HIV- men (OR = 3.2, 95% CI: 1.5-6.7), and almost twice the odds of AVC (1.8, 1.1-2.9). HIV serostatus was not associated with TAC or AVRC. AVRC was associated with higher Il-6 and sCD163 levels. TAC was associated with higher ICAM-1, TNF-α RII, and Il-6 levels. AVC and AVRC calcification were associated with presence of non-calcified plaque in HIV+ but not HIV- men.ConclusionHIV infection is an independent predictor of MAC and AVC. Whether these calcifications predict mortality in HIV+ patients deserves further investigation

Metabolic syndrome, fatty liver, and artificial intelligence-based epicardial adipose tissue measures predict long-term risk of cardiac events: a prospective study

BackgroundWe sought to evaluate the association of metabolic syndrome (MetS) and computed tomography (CT)-derived cardiometabolic biomarkers (non-alcoholic fatty liver disease [NAFLD] and epicardial adipose tissue [EAT] measures) with long-term risk of major adverse cardiovascular events (MACE) in asymptomatic individuals.MethodsThis was a post-hoc analysis of the prospective EISNER (Early-Identification of Subclinical Atherosclerosis by Noninvasive Imaging Research) study of participants who underwent baseline coronary artery calcium (CAC) scoring CT and 14-year follow-up for MACE (myocardial infarction, late revascularization, or cardiac death). EAT volume (cm3) and attenuation (Hounsfield units [HU]) were quantified from CT using fully automated deep learning software (&lt; 30&nbsp;s per case). NAFLD was defined as liver-to-spleen attenuation ratio &lt; 1.0 and/or average liver attenuation &lt; 40 HU.ResultsIn the final population of 2068 participants (59% males, 56 ± 9 years), those with MetS (n = 280;13.5%) had a greater prevalence of NAFLD (26.0% vs. 9.9%), higher EAT volume (114.1 cm3 vs. 73.7 cm3), and lower EAT attenuation (-76.9 HU vs. -73.4 HU; all p &lt; 0.001) compared to those without MetS. At 14 ± 3 years, MACE occurred in 223 (10.8%) participants. In multivariable Cox regression, MetS was associated with increased risk of MACE (HR 1.58 [95% CI 1.10-2.27], p = 0.01) independently of CAC score; however, not after adjustment for EAT measures (p = 0.27). In a separate Cox analysis, NAFLD predicted MACE (HR 1.78 [95% CI 1.21-2.61], p = 0.003) independently of MetS, CAC score, and EAT measures. Addition of EAT volume to current risk assessment tools resulted in significant net reclassification improvement for MACE (22% over ASCVD risk score; 17% over ASCVD risk score plus CAC score).ConclusionsMetS, NAFLD, and artificial intelligence-based EAT measures predict long-term MACE risk in asymptomatic individuals. Imaging biomarkers of cardiometabolic disease have the potential for integration into routine reporting of CAC scoring CT to enhance cardiovascular risk stratification. Trial registration NCT00927693

Machine learning integration of circulating and imaging biomarkers for explainable patient-specific prediction of cardiac events: A prospective study

Background and aimsWe sought to assess the performance of a comprehensive machine learning (ML) risk score integrating circulating biomarkers and computed tomography (CT) measures for the long-term prediction of hard cardiac events in asymptomatic subjects.MethodsWe studied 1069 subjects (age 58.2&nbsp;±&nbsp;8.2 years, 54.0% males) from the prospective EISNER trial who underwent coronary artery calcium (CAC) scoring CT, serum biomarker assessment, and long-term follow-up. Epicardial adipose tissue (EAT) was quantified from CT using fully automated deep learning software. Forty-eight serum biomarkers, both established and novel, were assayed. An ML algorithm (XGBoost) was trained using clinical risk factors, CT measures (CAC score, number of coronary lesions, aortic valve calcium score, EAT volume and attenuation), and circulating biomarkers, and validated using repeated 10-fold cross validation.ResultsAt 14.5&nbsp;±&nbsp;2.0 years, there were 50 hard cardiac events (myocardial infarction or cardiac death). The ML risk score (area under the receiver operator characteristic curve [AUC] 0.81) outperformed the CAC score (0.75) and ASCVD risk score (0.74; both p&nbsp;=&nbsp;0.02) for the prediction of hard cardiac events. Serum biomarkers provided incremental prognostic value beyond clinical data and CT measures in the ML model (net reclassification index 0.53 [95% CI: 0.23-0.81], p&nbsp;&lt;&nbsp;0.0001). Among novel biomarkers, MMP-9, pentraxin 3, PIGR, and GDF-15 had highest variable importance for ML and reflect the pathways of inflammation, extracellular matrix remodeling, and fibrosis.ConclusionsIn this prospective study, ML integration of novel circulating biomarkers and noninvasive imaging measures provided superior long-term risk prediction for cardiac events compared to current risk assessment tools