Echocardiographic features of carcinoid heart disease

We reviewed the records of the Mayo Clinic patients with known carcinoid syndrome in whom echocardiographic studies had been done. Nineteen patients had M-mode and 2-dimensional echocardiographic examinations, and 1 patient had an M-mode examination only. Of the 20 patients, 8 had no evidence by echocardiogram of carcinoid heart disease; 2 had changes in the tricuspid valve echogram suggestive of early carcinoid heart disease, and the other 10 patients had the following distinctive echocardiographic findings: (1) the pattern of right ventricular volume overload (enlarged right ventricle with abnormal septal motion); (2) abnormal right-sided valves, including (a) a striking appearance of the tricuspid valve, the leaflets appearing thickened, retracted, and fixed in a semiopen position throughout the cardiac cycle, and (b) thickened, retracted pulmonic valve cusps, when visualized; and (3) the left-sided valves and chambers rarely involved. These echocardiographic features are distinctive of advanced carcinoid heart disease and correlate closely with pathologic findings.     

Task-related motivation and academic achievement in children and adolescents with ADHD

European Child & Adolescent Psychiatry - Academic impairment in individuals with attention-deficit/hyperactivity disorder (ADHD) is in part due to reduced motivation for academic tasks, which...     

Deep Artificial Neural Networks Reveal a Distributed Cortical Network Encoding Propositional Sentence-Level Meaning

Understanding how and where in the brain sentence-level meaning is constructed from words presents a major scientific challenge. Recent advances have begun to explain brain activation elicited by sentences using vector models of word meaning derived from patterns of word co-occurrence in text corpora. These studies have helped map out semantic representation across a distributed brain network spanning temporal, parietal, and frontal cortex. However, it remains unclear whether activation patterns within regions reflect unified representations of sentence-level meaning, as opposed to superpositions of context-independent component words. This is because models have typically represented sentences as “bags-of-words” that neglect sentence-level structure. To address this issue, we interrogated fMRI activation elicited as 240 sentences were read by 14 participants (9 female, 5 male), using sentences encoded by a recurrent deep artificial neural-network trained on a sentence inference task (InferSent). Recurrent connections and nonlinear filters enable InferSent to transform sequences of word vectors into unified “propositional” sentence representations suitable for evaluating intersentence entailment relations. Using voxelwise encoding modeling, we demonstrate that InferSent predicts elements of fMRI activation that cannot be predicted by bag-of-words models and sentence models using grammatical rules to assemble word vectors. This effect occurs throughout a distributed network, which suggests that propositional sentence-level meaning is represented within and across multiple cortical regions rather than at any single site. In follow-up analyses, we place results in the context of other deep network approaches (ELMo and BERT) and estimate the degree of unpredicted neural signal using an “experiential” semantic model and cross-participant encoding.SIGNIFICANCE STATEMENT A modern-day scientific challenge is to understand how the human brain transforms word sequences into representations of sentence meaning. A recent approach, emerging from advances in functional neuroimaging, big data, and machine learning, is to computationally model meaning, and use models to predict brain activity. Such models have helped map a cortical semantic information-processing network. However, how unified sentence-level information, as opposed to word-level units, is represented throughout this network remains unclear. This is because models have typically represented sentences as unordered “bags-of-words.” Using a deep artificial neural network that recurrently and nonlinearly combines word representations into unified propositional sentence representations, we provide evidence that sentence-level information is encoded throughout a cortical network, rather than in a single region.     

Odontoblasts in odontogenic tumors

Odontoblasts are secretory cells displaying epithelial and mesenchymal features, which exist in a monolayer at the interface between the dentin and pulp of a tooth. During embryogenesis, these cells form a dentin shell and throughout life continue to produce dentin while, also acting as sensor cells helping to mediate tooth sensitivity. In this process, odontoblasts are forced to migrate inwards, resulting in an ongoing loss of pulp volume. Correspondingly, there is also a decrease in the surface area of the dentin which supports the odontoblast cell layer. As these events transpire, odontoblasts maintain a tightly controlled monolayer relationship to each other as well as to their dentin substrate. Stability is maintained laterally by epithelial attachment structures and transversely by complex cytoplasmic extensions into the supporting dentin. As a result, it is not possible for the layer to buckle to relieve the mechanical stresses, which develop during the inward migration. A theoretical consequence of this distinctive self-generated niche is the development of long term compressive stresses within the odontoblast population. We present a mechanobiology model, which causally relates the increase in cellular compressive stresses to contact inhibition of proliferation. We link this hypothesis to the observation that there are no reports of pulpal odontoblasts showing neoplasia or acquisition of changes suggestive of a pre-neoplastic phenotype.     

The vascular basis of the hemi-hamate osteochondral free flap. Part 2: surgical anatomy and clinical application

Purpose

The free vascularised hemi-hamate flap combines the utility of providing a small osteochondral portion of hamate for reconstruction, while providing a means of vascularisation to preserve articular cartilage after transfer. In Part 1 of this series, we highlighted the vascular approaches to such a technique. The current study investigates the bony architecture of the hamate, with particular emphasis on its utility for a range of osteochondral defects in the hand.

Methods

A morphometric assessment of the hamate as a potential osteochondral flap donor site for resurfacing digital phalangeal heads (either total or unicondylar) and/or bases was thus undertaken. This anatomic study was undertaken using in vivo imaging performed for a range of clinical indications, with computed tomographic angiography (CTA) and digital subtraction angiography (DSA) of the upper limb included. Bony and vascular measurements and relationships were recorded and assessed both quantitatively and qualitatively. A clinical case is presented, highlighting the application of these measurements.

Results

The mean digital artery diameter was 0.7 mm with a mean distance between digital artery and interphalangeal joint surface (i.e. pedicle length) of 1.18 mm. Mean hamate dimensions comprised a transverse width 16.62 mm, lateral width of 14.29 mm and ridge height of 1.43 mm. Measurements of the phalangeal bases, condyles and total phalangeal heads were recorded, and the optimal hamate harvest approaches demonstrated. Despite perceived differences, in all cases there was statistical similarity demonstrated between the fragments.

Conclusion

The hemi-hamate osteochondral flap can be applied to a range of osteochondral defects in the hand and may offer new options to the hand surgeon.     

The Time Course of Facial Expression Recognition Using Spatial Frequency Information: Comparing Pain and Core Emotions

We are able to recognize others’ experience of pain from their facial expressions. However, little is known about what makes the recognition of pain possible and whether it is similar or different from core emotions. This study investigated the mechanisms underpinning the recognition of pain expressions, in terms of spatial frequency (SF) information analysis, and compared pain with 2 core emotions (ie, fear and happiness). Two experiments using a backward masking paradigm were conducted to examine the time course of low- and high-SF information processing, by manipulating the presentation duration of face stimuli and target-mask onset asynchrony. Overall, we found a temporal advantage of low-SF over high-SF information for expression recognition, including pain. This asynchrony between low- and high-SF happened at a very early stage of information extraction, which indicates that the decoding of low-SF expression information is not only faster but possibly occurs before the processing of high-SF information. Interestingly, the recognition of pain was also found to be slower and more difficult than core emotions. It is suggested that more complex decoding process may be involved in the successful recognition of pain from facial expressions, possibly due to the multidimensional nature of pain experiences.PerspectiveTwo studies explore the perceptual and temporal properties of the decoding of pain facial expressions. At very early stages of attention, the recognition of pain was found to be more difficult than fear and happiness. It suggests that pain is a complex expression, and requires additional time to detect and process.