Sex differences in the biomechanical and biochemical responses of caudal rat intervertebral discs to injury

Background

Intervertebral disc degeneration (IDD) is a major cause of low back pain (LBP) worldwide. Sexual dimorphism, or sex-based differences, appear to exist in the severity of LBP. However, it is unknown if there are sex-based differences in the inflammatory, biomechanical, biochemical, and histological responses of intervertebral discs (IVDs).

Methods

Caudal (Coccygeal/Co) bone-disc-bone motion segments were isolated from multiple spinal levels (Co8 to Co14) of male and female Sprague-Dawley rats. Changes in motion segment biomechanics and extracellular matrix (ECM) biochemistry (glycosaminoglycan [GAG], collagen [COL], water, and DNA content) were evaluated at baseline and in response to chemical insult (lipopolysaccharide [LPS]) or puncture injury ex vivo. We also investigated the contributions of Toll-like receptor (TLR4) signaling on responses to LPS or puncture injury ex vivo, using a small molecule TLR4 inhibitor, TAK-242.

Results

Findings indicate that IVD motion segments from female donors had greater nitric oxide (NO) release in LPS groups compared to male donors. HMGB1 release was increased in punctured discs, but not LPS injured discs, with no sex effect. Although both male and female discs exhibited reductions in dynamic moduli in response to LPS and puncture injuries, dynamic moduli from female donors were higher than male donors across all groups. In uninjured (baseline) samples, a significant sex effect was observed in nucleus pulposus (NP) DNA and water content. Female annulus fibrosus (AF) also had higher DNA, GAG, and COL content (normalized by dry weight), but lower water content than male AF. Additional injury- and sex-dependent effects were observed in AF GAG/DNA and COL/DNA content. Finally, TAK-242 improved the dynamic modulus of female but not male punctured discs.

Conclusions

Our findings demonstrate that there are differences in rat IVD motion segments based on sex, and that the response to injury in inflammatory, biomechanical, biochemical, and histological outcomes also exhibit sex differences. TLR4 inhibition protected against loss of mechanical integrity of puncture-injured IVD motion segments, with differences responses based on donor sex.

Machine learning to automatically detect anatomical landmarks on phase-contrast enhanced magnetic resonance angiography

Funding Acknowledgements

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Guala A. received funding from the Spanish Ministry of Science, Innovation and Universities

Introduction

The heterogeneous characteristic of the thoracic aorta implies that all biomarkers with potential for risk stratification need to be references to a specific location. This is the case, for example, of diameter [1], stiffness [2] and wall shear stress [3]. This is normally achieved by the manual identification of a limited number of key anatomic landmarks [4], which is a time-demanding task and may impact biomarkers accuracy and reproducibility. Automatic identification of these anatomic landmarks may speed-up the analysis and allow for the creation of fully automatic image analysis pipelines. Machine learning (ML) algorithms might be suitable for this task.

Purpose

The aim of this study was to test the performance of a ML algorithm in localizing key thoracic anatomical landmarks on phase-contrast enhanced magnetic resonance angiograms (PC-MRA).

Methods

PC-MRA of 323 patients with native aorta and aortic valve and a variety of aortic conditions (141 bicuspid aortic valve patients, 60 patients with degenerative aortic aneurysms, 82 patients with genetic aortopathy and 40 healthy volunteers) were included in this study. Four anatomical landmarks were manually identified on PC-MRA by an experienced researcher: sinotubular junction, the pulmonary artery bifurcation and the first and third supra-aortic vessel braches. A reinforcement learning algorithm (DQN), combining Q-learning with deep neural networks, was trained. The algorithm was tested in a separate set of 30 PC-MRA with similar distribution of aortic conditions in which human intra-observer reproducibility was quantified. The distance between points was used as quality metric and human annotation was considered as ground-truth. Repeated-measures ANOVA was used for statistical testing.

Results

ML algorithm resulted in performance similar to the intra-observer variability obtained by the experienced human reader in the identification of the sinotubular junction (11.1 ± 8.6 vs 11.0 ± 8.1 mm, p = 0.949) and first (6.8 ± 5.6 vs 6.6 ± 3.9 mm, p = 0.886) and third (8.4 ± 7.4 vs 6.8 ± 4.0 mm, p = 0.161) supra-aortic vessels branches. However, the algorithm did not reach human-level performance in the localization of the pulmonary artery bifurcation (15.2 ± 13.1 vs 10.2 ± 7.0 mm, p = 0.008). The time needed to the ML algorithm to locate all points ranged between 0.8 and 1.6 seconds on a standard computer while manual annotation required around two minutes to be performed.

Conclusions

The rapid identification of key aortic anatomical landmarks by a reinforced learning algorithm is feasible with human-level performance. This approach may thus be used for the design of fully-automatic pipeline for 4D flow CMR analysis.

Reinforcement machine learning-based aortic anatomical landmarks detection from phase-contrast enhanced magnetic resonance angiography

Funding Acknowledgements

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Spanish Ministry of Science, Innovation and Universities; La Marató de TV3

Introduction

Automatic analysis of medical imaging data may improve their clinical impact by reducing analysis time and improving reproducibility. Many medical imaging data, like 4D-flow magnetic resonance imaging (MRI), are often quantified regionally, implying the need for anatomical landmark identification to locate correspondences in the extracted data and compare among patients. Machine learning (ML) techniques hold potential for automatic analysis of medical imaging. Phase-contrast enhanced magnetic resonance angiography (PC-MRA) is a class of angiograms not requiring the administration of contrast agents.

Purpose

We aimed to test whether a machine learning algorithm can be trained to identify key anatomical cardiovascular landmarks on PC-MRA images and compare its performance with humans.

Methods

Three-hundred twenty-three aortic PC-MRA were manually annotated with the location of 4 landmarks: sinotubular junction, pulmonary artery bifurcation and first and third supra-aortic vessels (Figure 1), often used to separate the aorta in sub-regions. Patients included in the training dataset comprised healthy volunteers (40), bicuspid aortic valve patients (141), patients with degenerative aortic disease (60) and patients with genetically-triggered aortic disease (82), all without previous aortic surgery and with native aortic valve. PC-MRA images and manual annotations were used to train a DQN, a reinforcement learning algorithm that combines Q-learning with deep neural networks. The agents can navigate the images and optimally find the landmarks by following the policies learned during training. Data from thirty patients, distributed in terms of aortic condition as the training set, unseen by the algorithm in the training phase, were used to quantify intra-observer reproducibility and to assess ML algorithm performance. Distance between points was used as metric for comparisons, original human annotation was used as ground-truth and repeated-measures ANOVA was used for statistical testing.

Results

Human and machine learning performed similarly in the identification of the sinotubular junction (distance between points of 11.0 ± 8.1 vs. 11.1 ± 8.6 mm, respectively, p = 0.949) and first (6.6 ± 3.9 vs. 6.8 ± 5.6 mm, p = 0.886) and third (6.8 ± 4.0 vs. 8.4 ± 7.4 mm, p = 0.161) supra-aortic vessels branches but human annotation outperformed ML landmark detection in the identification of the pulmonary artery bifurcation (10.2 ± 7.0 vs. 15.2 ± 13.1 mm, p = 0.008). Computation time for landmark detection by ML was between 0.8 and 1.6 seconds on a standard computer while human annotation took approximatively two minutes.

Conclusions

ML-based aortic landmarks detection from phase-contrast enhanced magnetic resonance angiography is feasible and fast and performs similarly to human. Reinforced learning anatomical landmark identification unlock automatic extraction of a variety of regional aortic data, including complex 4D flow parameters.

Abstract Figure

[Endoscopic ligation plus sclerotherapy in esophageal varices treatment. Experience in active bleeding and in the elective treatment]

BACKGROUND

Endoscopic ligation is the treatment of choice for bleeding esophageal varices. The usefulness of additional sclerotherapy is not clear.

AIM

To assess the effectiveness of ligation followed by sclerotherapy in the treatment of variceal bleeding.

PATIENTS AND METHODS

Forty eight patients with variceal bleeding admitted for emergency treatment and 73 patients admitted for elective treatment were studied. Varices were ligated until a significant reduction in size was achieved. Eradication was completed with the injection of 1% polidocanol.

RESULTS

In 34 of 48 patients admitted for emergency treatment, the site of variceal rupture was identified. In all these subjects, and in 13 of 14 patients in whom the rupture site was not identified, hemorrhage was stopped with the procedure. Varices were eradicated in 108 of the 121 patients. Hemorrhage recidivated in 12.5% of patients admitted for emergency treatment, in a period of 14 months of follow up and in 9.6% of those admitted for elective treatment, in a period of 16 months follow up. Mortality was 14, 18 and 57% among patients classified as Child Pugh A, B or C, respectively.

CONCLUSIONS

Endoscopic ligation is effective in the treatment of variceal bleeding. Adding sclerotherapy, variceal eradication is achieved in a high percentage of patients. Survival depends on hepatic function.

[Diagnosis and treatment of juvenile polyp of the colon]

In a five year period 75 colonoscopies were carried out in 66 children with rectal bleeding. Five patients presented with severe anemia. Their age ranged from 2 to 16 years (mean 6.6). Most of the procedures were performed on an outpatient basis (87%) and under oral sedative premedication (78%). At least the splenic flexure was reached in each case. We found 59 polyps in 47 children (71%). All polyps were snared and removed during the same procedure. There were no serious complications. Colonoscopy is a most usefull diagnostic and therapeutic procedure in children with rectal bleeding, where there is a high probability that a juvenile polyp can be found which can be safely treated during the same session.