Registration of preoperative temporal bone CT-scan to otoendoscopic video for augmented-reality based on convolutional neural networks

PURPOSE

Patient-to-image registration is a preliminary step required in surgical navigation based on preoperative images. Human intervention and fiducial markers hamper this task as they are time-consuming and introduce potential errors. We aimed to develop a fully automatic 2D registration system for augmented reality in ear surgery.

METHODS

CT-scans and corresponding oto-endoscopic videos were collected from 41 patients (58 ears) undergoing ear examination (vestibular schwannoma before surgery, profound hearing loss requiring cochlear implant, suspicion of perilymphatic fistula, contralateral ears in cases of unilateral chronic otitis media). Two to four images were selected from each case. For the training phase, data from patients (75% of the dataset) and 11 cadaveric specimens were used. Tympanic membranes and malleus handles were contoured on both video images and CT-scans by expert surgeons. The algorithm used a U-Net network for detecting the contours of the tympanic membrane and the malleus on both preoperative CT-scans and endoscopic video frames. Then, contours were processed and registered through an iterative closest point algorithm. Validation was performed on 4 cases and testing on 6 cases. Registration error was measured by overlaying both images and measuring the average and Hausdorff distances.

RESULTS

The proposed registration method yielded a precision compatible with ear surgery with a 2D mean overlay error of 0.65 ± 0.60 mm for the incus and 0.48 ± 0.32 mm for the round window. The average Hausdorff distance for these 2 targets was 0.98 ± 0.60 mm and 0.78 ± 0.34 mm respectively. An outlier case with higher errors (2.3 mm and 1.5 mm average Hausdorff distance for incus and round window respectively) was observed in relation to a high discrepancy between the projection angle of the reconstructed CT-scan and the video image. The maximum duration for the overall process was 18 s.

CONCLUSIONS

A fully automatic 2D registration method based on a convolutional neural network and applied to ear surgery was developed. The method did not rely on any external fiducial markers nor human intervention for landmark recognition. The method was fast and its precision was compatible with ear surgery.

Navigating the nuances: comparative analysis and hyperparameter optimisation of neural architectures on contrast-enhanced MRI for liver and liver tumour segmentation

In medical imaging, accurate segmentation is crucial to improving diagnosis, treatment, or both. However, navigating the multitude of available architectures for automatic segmentation can be overwhelming, making it challenging to determine the appropriate type of architecture and tune the most crucial parameters during dataset optimisation. To address this problem, we examined and refined seven distinct architectures for segmenting the liver, as well as liver tumours, with a restricted training collection of 60 3D contrast-enhanced magnetic resonance images (CE-MRI) from the ATLAS dataset. Included in these architectures are convolutional neural networks (CNNs), transformers, and hybrid CNN/transformer architectures. Bayesian search techniques were used for hyperparameter tuning to hasten convergence to the optimal parameter mixes while also minimising the number of trained models. It was unexpected that hybrid models, which typically exhibit superior performance on larger datasets, would exhibit comparable performance to CNNs. The optimisation of parameters contributed to better segmentations, resulting in an average increase of 1.7% and 5.0% in liver and tumour segmentation Dice coefficients, respectively. In conclusion, the findings of this study indicate that hybrid CNN/transformer architectures may serve as a practical substitute for CNNs even in small datasets. This underscores the significance of hyperparameter optimisation.

Segmentation of the aorta in systolic phase from 4D flow MRI: multi-atlas vs. deep learning

OBJECTIVE

In the management of the aortic aneurysm, 4D flow magnetic resonance Imaging provides valuable information for the computation of new biomarkers using computational fluid dynamics (CFD). However, accurate segmentation of the aorta is required. Thus, our objective is to evaluate the performance of two automatic segmentation methods on the calculation of aortic wall pressure.

METHODS

Automatic segmentation of the aorta was performed with methods based on deep learning and multi-atlas using the systolic phase in the 4D flow MRI magnitude image of 36 patients. Using mesh morphing, isotopological meshes were generated, and CFD was performed to calculate the aortic wall pressure. Node-to-node comparisons of the pressure results were made to identify the most robust automatic method respect to the pressures obtained with a manually segmented model.

RESULTS

Deep learning approach presented the best segmentation performance with a mean Dice similarity coefficient and a mean Hausdorff distance (HD) equal to 0.92+/- 0.02 and 21.02+/- 24.20 mm, respectively. At the global level HD is affected by the performance in the abdominal aorta. Locally, this distance decreases to 9.41+/- 3.45 and 5.82+/- 6.23 for the ascending and descending thoracic aorta, respectively. Moreover, with respect to the pressures from the manual segmentations, the differences in the pressures computed from deep learning were lower than those computed from multi-atlas method.

CONCLUSION

To reduce biases in the calculation of aortic wall pressure, accurate segmentation is needed, particularly in regions with high blood flow velocities. Thus, the deep learning segmen-tation method should be preferred.

Image-to-Patient Registration in Computer-Assisted Surgery of Head and Neck: State-of-the-Art, Perspectives, and Challenges

Today, image-guided systems play a significant role in improving the outcome of diagnostic and therapeutic interventions. They provide crucial anatomical information during the procedure to decrease the size and the extent of the approach, to reduce intraoperative complications, and to increase accuracy, repeatability, and safety. Image-to-patient registration is the first step in image-guided procedures. It establishes a correspondence between the patient's preoperative imaging and the intraoperative data. When it comes to the head-and-neck region, the presence of many sensitive structures such as the central nervous system or the neurosensory organs requires a millimetric precision. This review allows evaluating the characteristics and the performances of different registration methods in the head-and-neck region used in the operation room from the perspectives of accuracy, invasiveness, and processing times. Our work led to the conclusion that invasive marker-based methods are still considered as the gold standard of image-to-patient registration. The surface-based methods are recommended for faster procedures and applied on the surface tissues especially around the eyes. In the near future, computer vision technology is expected to enhance these systems by reducing human errors and cognitive load in the operating room.

Segmentation of 4D Flow MRI: Comparison between 3D Deep Learning and Velocity-Based Level Sets

A thoracic aortic aneurysm is an abnormal dilatation of the aorta that can progress and lead to rupture. The decision to conduct surgery is made by considering the maximum diameter, but it is now well known that this metric alone is not completely reliable. The advent of 4D flow magnetic resonance imaging has allowed for the calculation of new biomarkers for the study of aortic diseases, such as wall shear stress. However, the calculation of these biomarkers requires the precise segmentation of the aorta during all phases of the cardiac cycle. The objective of this work was to compare two different methods for automatically segmenting the thoracic aorta in the systolic phase using 4D flow MRI. The first method is based on a level set framework and uses the velocity field in addition to 3D phase contrast magnetic resonance imaging. The second method is a U-Net-like approach that is only applied to magnitude images from 4D flow MRI. The used dataset was composed of 36 exams from different patients, with ground truth data for the systolic phase of the cardiac cycle. The comparison was performed based on selected metrics, such as the Dice similarity coefficient (DSC) and Hausdorf distance (HD), for the whole aorta and also three aortic regions. Wall shear stress was also assessed and the maximum wall shear stress values were used for comparison. The U-Net-based approach provided statistically better results for the 3D segmentation of the aorta, with a DSC of 0.92 ± 0.02 vs. 0.86 ± 0.5 and an HD of 21.49 ± 24.8 mm vs. 35.79 ± 31.33 mm for the whole aorta. The absolute difference between the wall shear stress and ground truth slightly favored the level set method, but not significantly (0.754 ± 1.07 Pa vs. 0.737 ± 0.79 Pa). The results showed that the deep learning-based method should be considered for the segmentation of all time steps in order to evaluate biomarkers based on 4D flow MRI.

4D segmentation of the thoracic aorta from 4D flow MRI using deep learning

BACKGROUND

4D flow MRI allows the analysis of hemodynamic changes in the aorta caused by pathologies such as thoracic aortic aneurysms (TAA). For personalized management of TAA, new biomarkers are required to analyze the effect of fluid structure iteration which can be obtained from 4D flow MRI. However, the generation of these biomarkers requires prior 4D segmentation of the aorta.

OBJECTIVE

To develop an automatic deep learning model to segment the aorta in 4D from 4D flow MRI.

METHODS

Segmentation is addressed with a U-Net based segmentation model that treats each 4D flow MRI frame as an independent sample. Performance is measured with respect to Dice score (DS) and Hausdorff distance (HD). In addition, the maximum and minimum surface areas at the level of the ascending aorta are measured and compared with those obtained from cine-MRI.

RESULTS

The segmentation performance was 0.90 ± 0.02 for the DS and the mean HD was 9.58 ± 4.36 mm. A correlation coefficient of r = 0.85 was obtained for the maximum surface and r = 0.86 for the minimum surface between the 4D flow MRI and cine-MRI.

CONCLUSION

The proposed automatic approach of 4D aortic segmentation from 4D flow MRI seems to be accurate enough to contribute to the wider use of this imaging technique in the analysis of pathologies such as TAA.

LU-Net: A Multistage Attention Network to Improve the Robustness of Segmentation of Left Ventricular Structures in 2-D Echocardiography

Segmentation of cardiac structures is one of the fundamental steps to estimate volumetric indices of the heart. This step is still performed semiautomatically in clinical routine and is, thus, prone to interobserver and intraobserver variabilities. Recent studies have shown that deep learning has the potential to perform fully automatic segmentation. However, the current best solutions still suffer from a lack of robustness in terms of accuracy and number of outliers. The goal of this work is to introduce a novel network designed to improve the overall segmentation accuracy of left ventricular structures (endocardial and epicardial borders) while enhancing the estimation of the corresponding clinical indices and reducing the number of outliers. This network is based on a multistage framework where both the localization and segmentation steps are optimized jointly through an end-to-end scheme. Results obtained on a large open access data set show that our method outperforms the current best-performing deep learning solution with a lighter architecture and achieved an overall segmentation accuracy lower than the intraobserver variability for the epicardial border (i.e., on average a mean absolute error of 1.5 mm and a Hausdorff distance of 5.1mm) with 11% of outliers. Moreover, we demonstrate that our method can closely reproduce the expert analysis for the end-diastolic and end-systolic left ventricular volumes, with a mean correlation of 0.96 and a mean absolute error of 7.6 ml. Concerning the ejection fraction of the left ventricle, results are more contrasted with a mean correlation coefficient of 0.83 and an absolute mean error of 5.0%, producing scores that are slightly below the intraobserver margin. Based on this observation, areas for improvement are suggested.

Real-Time Automatic Ejection Fraction and Foreshortening Detection Using Deep Learning

Volume and ejection fraction (EF) measurements of the left ventricle (LV) in 2-D echocardiography are associated with a high uncertainty not only due to interobserver variability of the manual measurement, but also due to ultrasound acquisition errors such as apical foreshortening. In this work, a real-time and fully automated EF measurement and foreshortening detection method is proposed. The method uses several deep learning components, such as view classification, cardiac cycle timing, segmentation and landmark extraction, to measure the amount of foreshortening, LV volume, and EF. A data set of 500 patients from an outpatient clinic was used to train the deep neural networks, while a separate data set of 100 patients from another clinic was used for evaluation, where LV volume and EF were measured by an expert using clinical protocols and software. A quantitative analysis using 3-D ultrasound showed that EF is considerably affected by apical foreshortening, and that the proposed method can detect and quantify the amount of apical foreshortening. The bias and standard deviation of the automatic EF measurements were -3.6 ± 8.1%, while the mean absolute difference was measured at 7.2% which are all within the interobserver variability and comparable with related studies. The proposed real-time pipeline allows for a continuous acquisition and measurement workflow without user interaction, and has the potential to significantly reduce the time spent on the analysis and measurement error due to foreshortening, while providing quantitative volume measurements in the everyday echo lab.

Deep Learning for Segmentation Using an Open Large-Scale Dataset in 2D Echocardiography

Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the automation of this task has been the subject of intense research. In this paper, we evaluate how far the state-of-the-art encoder-decoder deep convolutional neural network methods can go at assessing 2D echocardiographic images, i.e., segmenting cardiac structures and estimating clinical indices, on a dataset, especially, designed to answer this objective. We, therefore, introduce the cardiac acquisitions for multi-structure ultrasound segmentation dataset, the largest publicly-available and fully-annotated dataset for the purpose of echocardiographic assessment. The dataset contains two and four-chamber acquisitions from 500 patients with reference measurements from one cardiologist on the full dataset and from three cardiologists on a fold of 50 patients. Results show that encoder-decoder-based architectures outperform state-of-the-art non-deep learning methods and faithfully reproduce the expert analysis for the end-diastolic and end-systolic left ventricular volumes, with a mean correlation of 0.95 and an absolute mean error of 9.5 ml. Concerning the ejection fraction of the left ventricle, results are more contrasted with a mean correlation coefficient of 0.80 and an absolute mean error of 5.6%. Although these results are below the inter-observer scores, they remain slightly worse than the intra-observer's ones. Based on this observation, areas for improvement are defined, which open the door for accurate and fully-automatic analysis of 2D echocardiographic images.

Total knee arthroplasty after opening- versus closing-wedge high tibial osteotomy. A 135-case series with minimum 5-year follow-up

INTRODUCTION

High tibial osteotomy (HTO) is effective in treating isolated medial osteoarthritis of the knee, but subsequent deterioration is inevitable, and total knee arthroplasty (TKA) is then an option. The present study sought to compare TKA following medial opening-wedge HTO (OW-HTO) versus lateral closing-wedge HTO (CW-HTO) in terms of intraoperative data and clinical results. The study hypothesis was that there is no significant difference in clinical results or complications in TKA following OW-HTO or CW-HTO.

MATERIAL AND METHOD

A retrospective multicenter (9 centers) study was conducted for the French Society of Orthopedic Surgery and Traumatology (SoFCOT), including 135 TKAs following HTO (58 OW and 77 CW) at a minimum 5 years' follow-up. Mean interval between HTO and TKA was 134 months and was longer in case of CW-HTO (P<0.0001). Mean age at TKA was 65.4 years and older in case of CW-HTO (P=0.021). Tibial slope was greater in case of OW-HTO (P=0.024). Prior to TKA, 55.7% of patients could walk without canes, 98.4% found stairs difficult or impossible and only 19.1% could manage a walking distance greater than 1000m. Mean flexion was 110°; 54.2% of patients showed frontal knee stability and 87.8% sagittal stability; 60.1% had a mechanical axis in varus, without difference according to OW- or CW-HTO.

RESULTS

Hardware was almost systematically removed (in 98.5% of cases): in the same step for OW-HTO (P=0.018) or often in 2 steps for CW-HTO. The primary approach was generally re-used (54.2%), but less frequently in the CW-HTO group (P=0.0004). Lateral or medial ligament release was not associated in respectively 78.2% and 79.7% of cases. The TKA implant was usually without stem (87.2%) and was fitted using a conventional technique (74.4%). At a mean 87 months' follow-up, 78.5% of patients could walk without canes, stairs were still difficult or impossible for 67%, and 74.1% could now walk further than 1000m; mean flexion was 110.5°. Overall, 91.5% of patients showed frontal knee stability and 98.2% sagittal stability, without difference according to OW- or CW-HTO. There were 15 complications within 3 months, more often in the OW-HTO group (12.3%) although not significantly, and with no difference in severity. Late complications comprised loosening (5.5%) and infection (3.6%) and were more frequent in the CW-HTO group (12%) (P<0.05).

DISCUSSION

The study hypothesis was partially confirmed. The only technical differences concerned hardware removal, often performed in two steps in case of CW-HTO, and TKA approach, which differed from the primary approach in case of CW-HTO. Clinical results were comparable between OW- and CW-HTO, but late complications were more frequent in the CW-HTO group.

LEVEL OF EVIDENCE

III; comparative retrospective study.

Daylight photodynamic therapy with methyl aminolevulinate cream is effective and nearly painless in treating actinic keratoses: a randomised, investigator-blinded, controlled, phase III study throughout Europe

BACKGROUND

Unmet needs exist in actinic keratosis (AK) treatment. Daylight photodynamic therapy (DL-PDT) has shown good efficacy and safety results compared to conventional PDT (c-PDT) in a recent Phase III multi-centre randomised controlled trial in Australia among 100 subjects with AKs.

OBJECTIVES

Demonstrate non-inferior efficacy and superior safety of DL-PDT compared to c-PDT in treating multiple mild and/or moderate facial/scalp AKs.

METHODS

Phase III, 12 week, multi-centre, randomised, investigator-blinded, controlled, intra-individual study conducted at different latitudes in Europe. AKs of adult subjects were treated once with methyl aminolevulinate (MAL) DL-PDT on one side of the face and MAL c-PDT contralaterally. Endpoints for DL-PDT concerned efficacy (non-inferiority regarding complete lesion response at week 12) and safety (superiority regarding subject's assessment of pain after treatment, on an 11-point numeric rating scale). Safety evaluation also included incidence of adverse events. Subject satisfaction was described using a questionnaire at baseline and last visit.

RESULTS

At week 12, the total lesion complete response rate with DL-PDT was similar (non-inferior) to c-PDT (70% vs. 74%, respectively; 95% CI [-9.5; 2.4] in PP analysis, confirmed in ITT analysis). In addition, efficacy of DL-PDT was demonstrated regardless of weather conditions (sunny or cloudy). DL-PDT was nearly painless compared to c-PDT (0.7 vs. 4.4, respectively; P < 0.001), better tolerated and resulted in higher subject satisfaction.

CONCLUSION

DL-PDT in comparison with c-PDT was as effective, better tolerated and nearly painless with high patient satisfaction, and may be considered a treatment of choice to meet needs of patients with mild or moderate facial/scalp AKs.

Vibration-induced compaction of granular suspensions

We investigate the compaction dynamics of vibrated granular suspensions using both digital imaging technique and MRI measurements. Starting from initialy loose packings, our experimental data suggest the existence of two stages in the compaction dynamics: a fast stage at short times where a rising compaction front propagates through the granular suspension and a slow stage at large times where the packing compacts slowly and homogeneously. The compaction dynamics in each stage can be well fitted to usual stretched exponential laws with stretching exponents equal to 2 and 0.45, respectively. The transition time between these two stages, τ c , depends on the fluid viscosity, vibration intensity and grain diameter. We show that τ c (-1) and the velocity of the front decrease roughly linearly with the lubrication Peclet number, Pe lub related to the competition between the lubrication stress induced by vibrations and the granular pressure.

Off-resonance effects and selectivity profiles in pulsed nitrogen-14 nuclear quadrupole resonance

In order to alleviate base-line distortions in nitrogen-14 NQR spectra originating from pulse breakthrough, low power radio-frequency (rf) pulses were applied. It is recalled that the required power is four times lower than that for an equivalent NMR experiment. This is easily explained by the fact that, in NMR, half the amplitude of the rf field is active. Moreover, the selectivity profile (i.e. the peak amplitude as a function of the difference between the carrier frequency and the resonance frequency) exhibits a shape which is, in most cases, more favorable in NQR than in NMR. An appropriate theory has been developed for explaining these experimental observations. It is concluded that low power NQR is perfectly feasible and should even be recommended for most applications, provided that the line-width of the NQR signal is not too large.

Vulvite par carence en fer

BACKGROUND

Many mucocutaneous signs associated with iron deficiency are described in the current dermatologic and haematologic literature but genital mucosa involvement has never been reported.

CASE REPORT

We report a case of iron deficiency anaemia revealed by psoriasis-like vulvar dermatitis. The vulvar involvement dramatically improved after iron therapy. In the case reported herein, iron deficiency resulted from three mechanisms: increased loss (menorrhagia), inadequate dietary iron intake (vegetarian diet) and inadequate absorption (iron absorption inhibitors such as tea).

DISCUSSION

Iron deficiency may be responsible for genital mucosa involvement. Iron deficiency investigation must determine the presence of blood loss and dietary habits (assessment of iron levels and ingestion of iron absorption inhibitors). Certain dietary recommendations are essential to avoid the failure of iron supplementation.

Recurrent Erysipelas: 47 Cases

BACKGROUND

Recurrence is a common complication of erysipelas (cellulitis).

OBJECTIVES

Todescribe the characteristics of patients with recurrent erysipelas and thereby, identify potential risk factors and evaluate prophylaxis efficacy.

METHODS

Data were retrospectively recorded from the files of 47 patients admitted to hospital between 1995 and 2003 for erysipelas recurrence. Studied variables included: general condition, regional and local factors, e.g. broken cutaneous barrier. Patient characteristics were used to construct tree-based models according to the classification and regression tree methodology.

RESULTS

Our patients suffered a mean of 4.1 recurrences. Cutaneous barrier disruption was observed in 81%, mainly intertrigo (60%). Antibiotic prophylaxis was taken by 68% of the patients for 30.6 months. After 1 and 2 years, 84 and 72% of the patients, respectively, were recurrence-free.

CONCLUSION

Our results showed that erysipelas recurrence has the same risk factors as single episodes and underlines the potential benefit of oral or parenteral antibiotic prophylaxis to prevent recurrences.

Venous thromboembolism prevention in surgery and obstetrics

BACKGROUND AND OBJECTIVE

To produce up-to-date clinical practice guidelines on the prevention of venous thromboembolism in surgery and obstetrics.

METHODS

A Steering Committee defined the scope of the topic, the questions to be answered, and the assessment criteria. Eight multidisciplinary working groups (total of 70 experts) performed a critical appraisal of the literature in the following disciplines: pharmacology of antithrombotic agents, orthopaedics; general surgery (gastrointestinal (GI) and varicose vein surgery); urology; gynaecology and obstetrics; thoracic, cardiac and vascular surgery; surgery of the head, neck and spine; and surgery of burns patients. The resultant reports and guidelines were submitted for comment and completion of the Appraisal of Guidelines Research & Evaluation questionnaire to a total of 150 peer reviewers, before producing definite guidelines.

RESULTS

The report answers the following questions for each type of surgery: (i) What is the venous thromboembolism incidence according to clinical and/or paraclinical criteria in the absence of prophylaxis? (with stratification of venous thromboembolism risk into low, moderate and high categories); (ii) What is the efficacy and safety of the prophylactic measures used? (iii) When should prophylaxis be introduced and how long should it last? (iv) Does ambulatory surgery affect efficacy and safety of prophylaxis?

CONCLUSIONS

Apart from answering the above questions, the guidelines provide a summary table for each discipline. This table stratifies types of surgery into the three risk categories, specifies the recommended prophylaxis for venous thromboembolism (pharmacological and/or mechanical) and grades each recommendation. In addition, whenever appropriate, the recommended prophylaxis is adjusted to low- and high-risk patients.

Secretion of Streptomyces tendae antifungal protein 1 by Lactococcus lactis

Lactococcus lactis, the model lactic acid bacterium, is a good candidate for heterologous protein production in both foodstuffs and the digestive tract. We attempted to produce Streptomyces tendae antifungal protein 1 (Afp1) in L. lactis with the objective of constructing a strain able to limit fungal growth. Since Afp1 activity requires disulfide bond (DSB) formation and since intracellular redox conditions are reportedly unfavorable for DSB formation in prokaryotes, Afp1 was produced as a secreted form. An inducible expression-secretion system was used to drive Afp1 secretion by L. lactis; Afp1 was fused or not with LEISSTCDA, a synthetic propeptide (LEISS) that has been described to be a secretion enhancer. Production of Afp1 alone was not achieved, but production of LEISS-Afp1 was confirmed by Western blot and immunodetection with anti-Afp1 antibodies. This protein (molecular mass: 9.8 kDa) is the smallest non-bacteriocin heterologous protein ever reported to be secreted in L. lactis via the Sec-dependent pathway. However, no anti-fungal activity was detected, even in concentrated samples of induced supernatant. This could be due to a too low secretion yield of Afp1 in L. lactis, to the absence of DSB formation, or to an improper DSB formation involving the additional cysteine residue included in LEISS propeptide. This raises questions about size limits, conformation problems, and protein secretion yields in L. lactis.

[Multinucleate cells angiohistiocytoma]

INTRODUCTION

Multinucleate cell angiohistiocytoma is a rare entity. We report two unusual cases of this lesion.

CASE REPORT

A 39 year-old man had presented since two years an arciform papular eruption of the forehead. A 40 year-old woman had a disseminated maculo-papular eruption since three years. Histological examination of skin samples of the two patients showed a dermal small vessel proliferation, associated with multinucleate cells, and confirmed the diagnosis of multinucleate cell angiohistiocytoma.

DISCUSSION

Our cases, typical histologically, have unusual clinical presentation (first case observed in a man, and the second in a disseminated form). Multinucleate cell angiohistiocytoma is a rare benign vascular proliferation individualized in 1985. Clinically, the lesion consists in small circumscribed papules. Acral sites and notably of the dorsum of the hands are the more frequent topography. It generally occurs in women aged over 50 years. Histologically, there is a dermal proliferation of capillaries and venules, and a fibrosis associated with the interstitial mononucleate cells and multinucleate cells.

NMR measurement of self-diffusion coefficients by slice selection

Most of the time, so-called inversion-recovery experiments concern longitudinal nuclear magnetization of the whole sample, the region of interest being limited by the transmitting-receiving coil. Here we address the question of what occurs if the region of interest is purposely limited to a thin slice selected by means of procedures employing magnetic field gradients. Gradients of both magnetic fields (B(0), the static magnetic field, and B(1), the radio-frequency magnetic field) can be used. In this study we resorted essentially to B(1) gradients and novel procedures, based on the natural inhomogeneity of the B(1) field delivered by a saddle coil, are described. It is obvious that molecules leaving and entering the slice during the evolution (recovery) period should influence the magnetization recovery. Molecular self-diffusion is responsible for such effects, experimentally visible and accounted for by an appropriate theory which has been approximated for by permitting an easy physical assessment. This approach should lead to alternative methods for measuring self-diffusion coefficients.

Recommendations for grading of concussion in athletes

Mild sports-related concussions, in which there is no loss of consciousness, account for >75% of all sports-related brain injury. Universal agreement on concussion definition and severity grading does not exist. Grading systems represent expertise of clinicians and researchers yet scientific evidence is lacking. Most used loss of consciousness and post-traumatic amnesia as markers for grading concussion. Although in severe head injury these parameters may have been proven important for prognosis, no study has done the same for sport-related concussion. Post-concussion symptoms are often the main features to help in the diagnosis of concussion in sport. Neuropsychological testing is meant to help physicians and health professionals to have objective indices of some of the neurocognitive symptoms. It is the challenge of physicians, therapists and coaches involved in the care of athletes to know the symptoms of concussion, recognise them when they occur and apply basic neuropsychological testing to help detect this injury. It is, therefore, recommended to be familiar with one grading system and use it consistently, even though it may not be scientifically validated. Then good clinical judgement and the ability to recognise post-concussion signs and symptoms will assure that an athlete never returns to play while symptomatic.

Members of the nuclear factor 1 family reduce the transcriptional potential of the nuclear receptor LXRalpha promoter

Expression of the LXRalpha nuclear receptor in liver is predicted to affect cholesterol and lipid metabolism. Here we show that a short fragment from the LXRalpha gene promoter spanning the region from -144 to +43 relative to the mRNA initiation site can drive transcription of a reporter gene. Under basal conditions, in vitro DNase I footprinting demonstrated interaction between nuclear proteins and an NF1 recognition site in close vicinity to the transcriptional initiation. Both supershift, mutational analyses in EMSA and transfections provided evidence that the NF1 (nuclear factor I) transcription factor interacts with the LXRalpha promoter. All four members of the NF1 family were found to suppress the transcriptional activity indicating a general inhibitory effect on LXRalpha expression. A similar regulation by NF1 was also observed when using a fragment from the LXRalpha promoter extending up to position -3033 therefore giving the inhibitory effect of NF1 a significant impact on LXRalpha gene expression.