Clinical stage and plasma neurofilament concentration in adults with Friedreich ataxia.

Objectives: Friedreich Ataxia (FRDA) is the most common recessive ataxia disorder. Yet, little is known of the prevalence in Sweden. In the future, there may be effective disease-modifying therapies, and use of clinical rating scales as well as possible biomarkers in serum or cerebrospinal fluid may be of importance. We evaluated the axonal protein neurofilament light in plasma (p-NfL) as a possible biomarker for disease severity in FRDA. Materials & methods: We searched for all possible genetically confirmed FRDA cases in the Västra Götaland Region (VGR) of Sweden, and investigated each patient clinically and obtained blood sample for analysis of p-NfL. Results: We found eight patients corresponding to 1/170.000 adults in the VGR, and 5 of these participated in the study. Three out of the five FRDA patients displayed a small or moderate increase in the p-NfL value, compared to the age-adjusted cut-offs for p-NfL established in the Clinical Neurochemistry Laboratory at our hospital. The two others were the oldest and most severely affected, displayed normal values according the cut-off values. The cohort is too small to make any statistically significant correlation between the five p-NfL values with regard to disease severity. Conclusions: FRDA is less prevalent in our region of Sweden than could be assumed. In concordance with previous studies from other authors, we find that p-NfL may be increased in patients with FRDA, but less so in older more clinically affected patients. Thus, we conclude that on an individual basis, p-NFL is of uncertain clinical value as a suitable biomarker.

No increase of serum neurofilament light in relapsing-remitting multiple sclerosis patients switching from standard to extended-interval dosing of natalizumab.

BACKGROUND: Accumulating evidence supports the efficacy of administering natalizumab (NZ) with extended-interval dosing (EID) in patients with relapsing-remitting multiple sclerosis (RRMS). OBJECTIVES: We switched NZ dosing from 4-week to 6-week intervals in patients with RRMS, and investigated the effect on serum neurofilament light chain (sNfL) concentrations. METHODS: We included two cohorts of patients with RRMS treated with NZ: one received the standard-interval dosing (4 weeks) at baseline, and were switched to 6-week intervals (EID4-6, N = 45). The other cohort received EID (5- or 6-week intervals) both at baseline and during follow-up (EID5/6, N = 25). Serum samples were collected in the EID4-6 cohort at every NZ infusion, for 12 months. The primary outcome was the change in sNfL concentrations after switching to EID. RESULTS: The baseline mean sNfL concentration in the EID4-6 cohort was 10.5 ng/L (standard deviation (SD) = 6.1), and it remained unchanged at 12 months. Moreover, individual sNfL concentrations did not change significantly after extending the NZ dosing intervals. In addition, the EID4-6 and EID5/6 cohorts had similar baseline sNfL concentrations. CONCLUSION: We concluded that extending the NZ dosing interval did not increase axonal damage, as determined with sNfL, in patients with RRMS.

SARS-COV-2 a trigger of myelin oligodendrocyte glycoprotein-associated disorder.

SARS-COV-2 frequently cause neurological disorders and is sometimes associated with onset of autoimmune diseases affecting the nervous system. Over recent years, a rare but distinct diagnosis designated myelin oligodendrocyte glycoprotein-associated disorder (MOGAD) has been recognized in patients with attacks of optic neuritis, myelitis, or encephalomyelitis and increased levels of anti-MOG antibodies. The cause of MOGAD is unknown. However, there have been reports of single cases of MOGAD in patients with Covid-19 infection. We report a series of SARS-CoV-2 positive patients that developed MOGAD, but a homology search did not support a cross-reactive immune response to SARS-CoV-2 spike-protein and MOG.

[Cervical myelopathy due to nitrous oxide inhalation]. / Omfattande inhalationsbruk av lustgas gav ryggmärgsskada.

A 19 year old male presented to his GP with bilateral numbness and stiffness of hands and lower limbs, as well as muscle weakness and poor balance. The patient admitted recreational use of nitrous oxide (laughing gas) some days earlier. He was hospitalised and underwent a series of plasmapheresis treatments due to an initial suspicion of inflammatory myelitis. Further investigation gave evidence of cervical myelopathy which was deemed secondary to heavy use of nitrous oxide. Substitution therapy with hydroxycobalamine was initiated and the patient gradually recovered, although he was later found to have hyperhomocysteinaemia. The adverse effects of recreational nitrous oxide use are discussed, as well as potential pitfalls in diagnosis.

Collaborative Working Architecture for IoT-Based Applications.

The new sensing applications need enhanced computing capabilities to handle the requirements of complex and huge data processing. The Internet of Things (IoT) concept brings processing and communication features to devices. In addition, the Cloud Computing paradigm provides resources and infrastructures for performing the computations and outsourcing the work from the IoT devices. This scenario opens new opportunities for designing advanced IoT-based applications, however, there is still much research to be done to properly gear all the systems for working together. This work proposes a collaborative model and an architecture to take advantage of the available computing resources. The resulting architecture involves a novel network design with different levels which combines sensing and processing capabilities based on the Mobile Cloud Computing (MCC) paradigm. An experiment is included to demonstrate that this approach can be used in diverse real applications. The results show the flexibility of the architecture to perform complex computational tasks of advanced applications.

Semen parameters can be predicted from environmental factors and lifestyle using artificial intelligence methods.

Fertility rates have dramatically decreased in the last two decades, especially in men. It has been described that environmental factors as well as life habits may affect semen quality. In this paper we use artificial intelligence techniques in order to predict semen characteristics resulting from environmental factors, life habits, and health status, with these techniques constituting a possible decision support system that can help in the study of male fertility potential. A total of 123 young, healthy volunteers provided a semen sample that was analyzed according to the World Health Organization 2010 criteria. They also were asked to complete a validated questionnaire about life habits and health status. Sperm concentration and percentage of motile sperm were related to sociodemographic data, environmental factors, health status, and life habits in order to determine the predictive accuracy of a multilayer perceptron network, a type of artificial neural network. In conclusion, we have developed an artificial neural network that can predict the results of the semen analysis based on the data collected by the questionnaire. The semen parameter that is best predicted using this methodology is the sperm concentration. Although the accuracy for motility is slightly lower than that for concentration, it is possible to predict it with a significant degree of accuracy. This methodology can be a useful tool in early diagnosis of patients with seminal disorders or in the selection of candidates to become semen donors.

Using GNG to improve 3D feature extraction--application to 6DoF egomotion.

Several recent works deal with 3D data in mobile robotic problems, e.g. mapping or egomotion. Data comes from any kind of sensor such as stereo vision systems, time of flight cameras or 3D lasers, providing a huge amount of unorganized 3D data. In this paper, we describe an efficient method to build complete 3D models from a Growing Neural Gas (GNG). The GNG is applied to the 3D raw data and it reduces both the subjacent error and the number of points, keeping the topology of the 3D data. The GNG output is then used in a 3D feature extraction method. We have performed a deep study in which we quantitatively show that the use of GNG improves the 3D feature extraction method. We also show that our method can be applied to any kind of 3D data. The 3D features obtained are used as input in an Iterative Closest Point (ICP)-like method to compute the 6DoF movement performed by a mobile robot. A comparison with standard ICP is performed, showing that the use of GNG improves the results. Final results of 3D mapping from the egomotion calculated are also shown.

Internal simulation of perceptions and actions.

We present a study of neural network architectures able to internally simulate perceptions and actions. All these architectures employ the novel Associative Self-Organizing Map (A-SOM) as a perceptual neural network. The A-SOM develops a representation of its input space, but in addition also learns to associate its activity with an arbitrary number of additional (possibly delayed) inputs. One architecture is a bimodal perceptual architecture whereas the others include an action neural network adapted by the delta rule. All but one architecture are recurrently connected. We have tested the architectures with very encouraging simulation results. The bimodal perceptual architecture was able to simulate appropriate sequences of activity patterns in the absence of sensory input for several epochs in both modalities. The architecture without recurrent connections correctly classified 100% of the training samples and 80% of the test samples. After ceasing to receive any input the best of the architectures with recurrent connections was able to continue to produce 100% correct output sequences for 28 epochs (280 iterations), and then to continue with 90% correct output sequences until epoch 42.