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A 79-year-old male with chronic coronary syndrome with complex coronary artery disease was included in the first-in-man trial of surgical revascularization guided solely by coronary computed tomography angiography (CCTA) and fractional flow reserve derived from CCTA (FFRCT). In CCTA analysis, the patient had calcified three-vessel disease, with a global anatomical SYNTAX score of 27. In contrast, in the initial FFRCT, only the ramus intermediate stenosis was physiologically significant, with no other vessels having an FFRCT ≤ 0.80 (functional SYNTAX score of 2). Discordance between the results of the CCTA and FFRCT necessitated an in-depth analysis by using both invasive and non-invasive coronary angiography. Angiography-derived fractional flow reserve (FFR) confirmed that the stenosis in the proximal left anterior descending artery (LAD) was physiologically significant, while it remained functionally negative in the second assessment of FFRCT. Extensive calcification is the most plausible explanation for the underestimation of the stenosis of proximal LAD in CCTA-derived FFR technology.

The current study investigated the bottom-up experiential emotion regulation in comparison to the cognitiveve top down-approach of cognitive defusion. Rooted in an experiential- and client-centered psychotherapeutic approach, experiential emotion regulation involves an active, non-intervening, accepting, open and welcoming approach towards the bodily felt affective experience in a welcoming, compassionate way, expressed in ‘experiential awareness’ in a first phase, and its verbalization or ‘experiential expression’ in a second phase. Defusion refers to the ability to observe one’s thoughts and feelings in a detached manner. Nineteen healthy participants completed an emotion regulation task during fMRI scanning by processing highly arousing negative events by images. Both experiential emotion regulation and cognitive defusion resulted in higher negative emotion compared to a ‘watch’ control condition. On the neurophysiological level, experiential emotion regulation recruited brain areas that regulate attention towards affective- and somatosensorial experience such as the anterior cingulate cortex, the paracingulate gyrus, the inferior frontal gyrus, and the prefrontal pole, areas underlying multisensory information integration (e.g., angular gyrus), and linking body states to emotion recognition and awareness (e.g., postcentral gyrus). Experiential emotion regulation, relative to the control condition, also resulted in a higher interaction between the anterior insular cortex and left amygdala while participants experienced less negative emotion. Cognitive defusion decreased activation in the subcortical areas such as the brainstem, the thalamus, the amygdala, and the hippocampus. In contrast to cognitive defusion, experiential emotion regulation relative to demonstrated greater activation in the left angular gyrus, indicating more multisensory information integration. These findings provide insight into different and specific neural networks underlying psychotherapy-based experiential emotion regulation and cognitive defusion.

We have read with interest the comment by Vankrunkelsven P. [...]

Multiple sclerosis (MS) manifests heterogeneously among persons suffering from it, making its disease course highly challenging to predict. At present, prognosis mostly relies on biomarkers that are unable to predict disease course on an individual level. Machine learning is a promising technique, both in terms of its ability to combine multimodal data and through the capability of making personalized predictions. However, most investigations on machine learning for prognosis in MS were geared towards predicting physical deterioration, while cognitive deterioration, although prevalent and burdensome, remained largely overlooked. This review aims to boost the field of machine learning for cognitive prognosis in MS by means of an introduction to machine learning and its pitfalls, an overview of important elements for study design, and an overview of the current literature on cognitive prognosis in MS using machine learning. Furthermore, the review discusses new trends in the field of machine learning that might be adopted for future studies in the field.

Long coronavirus disease 2019 (COVID-19) was described in patients recovering from COVID-19, with dyspnea being a frequent symptom. Data regarding the potential mechanisms of long COVID remain scarce. We investigated the presence of subclinical cardiac dysfunction, assessed by transthoracic echocardiography (TTE), in recovered COVID-19 patients with or without dyspnea, after exclusion of previous cardiopulmonary diseases. A total of 310 consecutive COVID-19 patients were prospectively included. Of those, 66 patients (mean age 51.3 ± 11.1 years, almost 60% males) without known cardiopulmonary diseases underwent one-year follow-up consisting of clinical evaluation, spirometry, chest computed tomography, and TTE. From there, 23 (34.8%) patients reported dyspnea. Left ventricle (LV) ejection fraction was not significantly different between patients with or without dyspnea (55.7 ± 4.6 versus (vs.) 57.6 ± 4.5, p = 0.131). Patients with dyspnea presented lower LV global longitudinal strain, global constructive work (GCW), and global work index (GWI) compared to asymptomatic patients (−19.9 ± 2.1 vs. −21.3 ± 2.3 p = 0.039; 2183.7 ± 487.9 vs. 2483.1 ± 422.4, p = 0.024; 1960.0 ± 396.2 vs. 2221.1 ± 407.9, p = 0.030). GCW and GWI were inversely and independently associated with dyspnea (p = 0.035, OR 0.998, 95% CI 0.997–1.000; p = 0.040, OR 0.998, 95% CI 0.997–1.000). Persistent dyspnea one-year after COVID-19 was present in more than a third of the recovered patients. GCW and GWI were the only echocardiographic parameters independently associated with symptoms, suggesting a decrease in myocardial performance and subclinical cardiac dysfunction.

Dynamic computer tomography (CT) is an emerging modality to analyze in-vivo joint kinematics at the bone level, but it requires manual bone segmentation and, in some instances, landmark identification. The objective of this study is to present an automated workflow for the assessment of three-dimensional in vivo joint kinematics from dynamic musculoskeletal CT images. The proposed method relies on a multi-atlas, multi-label segmentation and landmark propagation framework to extract bony structures and detect anatomical landmarks on the CT dataset. The segmented structures serve as regions of interest for the subsequent motion estimation across the dynamic sequence. The landmarks are propagated across the dynamic sequence for the construction of bone embedded reference frames from which kinematic parameters are estimated. We applied our workflow on dynamic CT images obtained from 15 healthy subjects on two different joints: thumb base (n = 5) and knee (n = 10). The proposed method resulted in segmentation accuracies of 0.90 ± 0.01 for the thumb dataset and 0.94 ± 0.02 for the knee as measured by the Dice score coefficient. In terms of motion estimation, mean differences in cardan angles between the automated algorithm and manual segmentation, and landmark identification performed by an expert were below 1°. Intraclass correlation (ICC) between cardan angles from the algorithm and results from expert manual landmarks ranged from 0.72 to 0.99 for all joints across all axes. The proposed automated method resulted in reproducible and reliable measurements, enabling the assessment of joint kinematics using 4DCT in clinical routine.

Introduction: No standard protocol for surveillance for melanoma patients is established. Whole-body magnetic resonance imaging (whole-body MRI) is a safe and sensitive technique that avoids exposure to X-rays and contrast agents. This prospective study explores the use of whole-body MRI for the early detection of recurrences. Material and Methods: Patients with American Joint Committee on Cancer Staging Manual (seventh edition; AJCC-7) stages IIIb/c or -IV melanoma who were disease-free following resection of macrometastases (cohort A), or obtained a durable complete response (CR) or partial response (PR) following systemic therapy (cohort B), were included. All patients underwent whole-body MRI, including T1, Short Tau Inversion Recovery, and diffusion-weighted imaging, every 4 months the first 3 years of follow-up and every 6 months in the following 2 years. A total body skin examination was performed every 6 months. Results: From November 2014 to November 2019, 111 patients were included (four screen failures, cohort A: 68 patients; cohort B: 39 patients). The median follow-up was 32 months. Twenty-six patients were diagnosed with suspected lesions. Of these, 15 patients were diagnosed with a recurrence on MRI. Eleven suspected lesions were considered to be of non-neoplastic origin. In addition, nine patients detected a solitary subcutaneous metastasis during self-examination, and two patients presented in between MRIs with recurrences. The overall sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were, respectively, 58%, 98%, 58%, 98%, and 98%. Sensitivity and specificity for the detection of distant metastases was respectively 88% and 98%. No patient experienced a clinically meaningful (>grade 1) adverse event. Conclusions: Whole-body MRI for the surveillance of melanoma patients is a safe and sensitive technique sparing patients′ cumulative exposure to X-rays and contrast media.