Herpes simplex encephalitis in adult patients with MASP-2 deficiency.

We report here two cases of Herpes simplex virus encephalitis (HSE) in adult patients with very rare, previously uncharacterized, non synonymous heterozygous G634R and R203W substitution in mannan-binding lectin serine protease 2 (MASP2), a gene encoding a key protease of the lectin pathway of the complement system. None of the 2 patients had variants in genes involved in the TLR3-interferon signaling pathway. Both MASP2 variants induced functional defects in vitro, including a reduced (R203W) or abolished (G634R) protein secretion, a lost capability to cleave MASP-2 precursor into its active form (G634R) and an in vivo reduced antiviral activity (G634R). In a murine model of HSE, animals deficient in mannose binding lectins (MBL, the main pattern recognition molecule associated with MASP-2) had a decreased survival rate and an increased brain burden of HSV-1 compared to WT C57BL/6J mice. Altogether, these data suggest that MASP-2 deficiency can increase susceptibility to adult HSE.

Identifying Fall Risk Predictors by Monitoring Daily Activities at Home Using a Depth Sensor Coupled to Machine Learning Algorithms.

Because of population ageing, fall prevention represents a human, economic, and social issue. Currently, fall-risk is assessed infrequently, and usually only after the first fall occurrence. Home monitoring could improve fall prevention. Our aim was to monitor daily activities at home in order to identify the behavioral parameters that best discriminate high fall risk from low fall risk individuals. Microsoft Kinect sensors were placed in the room of 30 patients temporarily residing in a rehabilitation center. The sensors captured the patients' movements while they were going about their daily activities. Different behavioral parameters, such as speed to sit down, gait speed or total sitting time were extracted and analyzed combining statistical and machine learning algorithms. Our algorithms classified the patients according to their estimated fall risk. The automatic fall risk assessment performed by the algorithms was then benchmarked against fall risk assessments performed by clinicians using the Tinetti test and the Timed Up and Go test. Step length, sit-stand transition and total sitting time were the most discriminant parameters to classify patients according to their fall risk. Coupling step length to the speed required to stand up or the total sitting time gave rise to an error-less classification of the patients, i.e., to the same classification as that of the clinicians. A monitoring system extracting step length and sit-stand transitions at home could complement the clinicians' assessment toolkit and improve fall prevention.

Automating the Timed Up and Go Test Using a Depth Camera.

Fall prevention is a human, economic and social issue. The Timed Up and Go (TUG) test is widely used to identify individuals with a high fall risk. However, this test has been criticized because its "diagnostic" is too dependent on the conditions in which it is performed and on the healthcare professionals running it. We used the Microsoft Kinect ambient sensor to automate this test in order to reduce the subjectivity of outcome measures and to provide additional information about patient performance. Each phase of the TUG test was automatically identified from the depth images of the Kinect. Our algorithms accurately measured and assessed the elements usually measured by healthcare professionals. Specifically, average TUG test durations provided by our system differed by only 0.001 s from those measured by clinicians. In addition, our system automatically extracted several additional parameters that allowed us to accurately discriminate low and high fall risk individuals. These additional parameters notably related to the gait and turn pattern, the sitting position and the duration of each phase. Coupling our algorithms to the Kinect ambient sensor can therefore reliably be used to automate the TUG test and perform a more objective, robust and detailed assessment of fall risk.

Automatic measurement of fall risk indicators in timed up and go test.

Fall risk assessment is usually conducted in specialized centers using clinical tests. Most of the time, these tests are performed only after the occurrence of health problems potentially affecting gait and posture stability. Our aim is to define fall risk indicators that could routinely be used at home to automatically monitor the evolution of fall risk over time. We used the standard Timed Up and Go (T.U.G.) test to classify 43 individuals into two classes of fall risk, namely high- vs low- risk. Several parameters related to the gait pattern and the sitting position included in the T.U.G. test were automatically extracted using an ambient sensor (Microsoft Kinect sensor). We were able to correctly classify all individuals using machine learning on the combination of two parameters among gait speed, step length and speed to sit down. Coupled to an ambient sensor installed at home to monitor the relevant parameters in daily activities, these algorithms could therefore be used to assess the evolution of fall risk, thereby improving fall prevention.

Planning of spatially-oriented locomotion following focal brain damage in humans: A pilot study.

Motor impairments in human gait following stroke or focal brain damage are well documented. Here, we investigated whether stroke and/or focal brain damage also affect the navigational component of spatially oriented locomotion. Ten healthy adult participants and ten adult brain-damaged patients had to walk towards distant targets from different starting positions (with vision or blindfolded). No instructions as to which the path to follow were provided to them. We observed very similar geometrical forms of paths across the two groups of participants and across visual conditions. This spatial stereotypy of whole-body displacements was observed following brain damage, even in the most severely impaired (hemiparetic) patients. This contrasted with much more variability at the temporal level. In particular, healthy participants and non-hemiparetic patients varied their walking speed according to curvature changes along the path. On the contrary, the walking speed profiles were not stereotypical and were not systematically constrained by path geometry in hemiparetic patients where it was associated with different stepping behaviors. These observations confirm the dissociation between cognitive and motor aspects of gait recovery post-stroke. The impact of these findings on the understanding of the functional and anatomical organization of spatially-oriented locomotion and for rehabilitation purposes is discussed and contextualized in the light of recent advances in electrophysiological studies.