A Five-Year Retrospective Analysis of Diagnostic and Treatment Data of Flexor Sheath Infections: Can We Accurately Predict the Presence and Severity of Infection Prior to Surgical Washout?

Flexor sheath infections (FSIs) are soft tissue infections affecting the hand, which, if mismanaged, can have devastating consequences. Clinical assessment is key to diagnosis, with many relying on Kanavel cardinal signs as an aid. To prevent unnecessary operative intervention and the associated post-operative combined patient and healthcare burden, it is key that patients with FSIs are correctly identified. It would also be useful to stratify severity of FSIs without surgical exploration. To date, there is no accepted method to assist clinicians in doing so. We retrospectively analysed data from a five-year period to see if we could identify pre-operatively (a) accurate predictors of FSIs and (b) severity of the FSIs. We established that only the presence of all four Kanavel cardinal signs significantly predicted the presence of an FSI. No other variable that was available prior to surgery could predict either presence or severity of infection.

Why Mechanism Matters: A Literature Review of Simultaneous Ipsilateral Tibial Tuberosity Avulsion and Patella Fracture With Case Report.

Simultaneous ipsilateral tibial tuberosity avulsion and patella fractures are rare in adults. They are often associated with patients who have underlying bone disease and other medical co-morbidities. Here we describe a case where this injury was attributed to direct trauma and demonstrate our department's management for such an injury. In addition to our case report, we have performed a systematic literature review to identify other cases of the same injury. Only four other cases have been reported. Here we summarise and compare the management and outcome measures reported in each case. All patients are managed differently, yet all authors report satisfactory outcomes. With this being a relatively rare injury, further research is required to establish a gold standard for management of such patients.

Atraumatic Bilateral Acute Compartment Syndrome of the Lower Legs: A Review of the Literature.

Bilateral acute compartment syndrome of the legs is a very rare presentation that requires emergency surgical intervention. Atraumatic bilateral cases are almost unheard of in medicine. There is currently no link between compartment syndrome and cognitive impairment or mental health. A systematic literature search was performed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines using the following keywords in multiple databases: compartment syndrome, atraumatic, spontaneous, bilateral, both, lower leg, acute, compartmental pressure, and fasciotomy. Atraumatic, bilateral, acute, and confirmed compartment syndrome cases were included. In total, 33 cases of atraumatic bilateral acute compartment syndrome (ABACS) were identified, of those 72.7% of cases were males. A form of cognitive impairment was found in 66% of cases. The medical history of the cases included substance abuse (nine patients), mental health illness (seven patients), and hypothyroidism (four patients). Within the reports, there was evidence of a misdiagnosis or delayed management in 19 cases (57.6%). Creatinine kinase (CK) was measured in 28 cases with a mean CK of 110,893 IU/L. Compartment pressure measurements were used in only 12 cases. A total of 29 cases were managed with bilateral four-compartment fasciotomy. This review highlights that ABACS is a condition with high rates of misdiagnosis or delay in treatment. Associations found included patients with cognitive impairment on presentation, mental health conditions, substance misuse, and elevated levels of CK. In addition, this review demonstrates that this condition is less rare than previously thought with serious morbidity and mortality.

Ticks on the Channel Islands and implications for public health.

The Channel Islands are British Crown dependencies located in the English Channel to the west of the Normandy coast in northern France. Whilst there have been studies investigating tick occurrence and distribution in different habitats on the mainland of the UK and in France, the Channel Islands have been relatively understudied. As such, little is known about whether the sheep tick, Ixodes ricinus, is present, and whether there is a potential risk of Lyme borreliosis on the Channel Islands. To ascertain the presence of I. ricinus on the three largest islands in the archipelago: Jersey, Guernsey and Alderney, surveys of ticks questing in the vegetation and ticks feeding on hosts were undertaken during April and May 2016. Across all three islands, the highest numbers of ticks were found in woodland habitats. Ixodes ricinus was the predominant questing tick species found on Jersey, and Ixodes ventalloi the most common questing tick species on Alderney and Guernsey, with little or no evidence of questing I. ricinus on either island. During field studies on small mammals, I. ricinus was the predominant tick species feeding on Jersey bank voles (Myodes glareolus caesarius), with Ixodes hexagonus the most common species infesting hedgehogs on Guernsey. We propose that the greater diversity of small mammals on Jersey may be important in supporting immature stages of I. ricinus, in contrast to Guernsey and Alderney. Morphological identification of tick species was confirmed by PCR sequencing based on amplification of the cytochrome c oxidase subunit one (cox1) gene (COI DNA barcoding). To date, there have been few records of human tick bites in the Channel Islands, suggesting that the current risk from tick-borne disease may be low, but continued reporting of any human tick bites, along with reporting of cases of Lyme borreliosis will be important for continued assessment of the impact of tick-borne diseases in the Channel Islands.

The analytic common spatial patterns method for EEG-based BCI data.

One of the most important stages in a brain-computer interface (BCI) system is that of extracting features that can reliably discriminate data recorded during different user states. A popular technique used for feature extraction in BCIs is the common spatial patterns (CSP) method, which provides a set of spatial filters that optimally discriminate between two classes of data in the least-squares sense. The method also yields a set of spatial patterns that are associated with the most relevant activity for distinguishing between the two classes. The high recognition rates that have been achieved with the method have led to its widespread adoption in the field. Here, a variant of the CSP method that considers EEG data in its complex form is described. By explicitly considering the amplitude and phase information in the data, the analytic CSP (ACSP) technique can provide a more comprehensive picture of the underlying activity, resulting in improved classification accuracies and more informative spatial patterns than the conventional CSP method. In this paper, we elaborate on the theoretical aspects of the ACSP algorithm and demonstrate the advantages of the method through a number of simulations and through tests on EEG data.

Complex-valued spatial filters for SSVEP-based BCIs with phase coding.

Brain-computer interface (BCI) systems based on steady-state visual evoked potentials (SSVEPs) have gained considerable popularity because of the robustness and high information transfer rate these can provide. Typical SSVEP setups make use of visual targets flashing at different frequencies, where a user's choice is determined from the SSVEPs elicited by the user gazing at a specific target. The range of stimulus frequencies available for such setups is limited by a variety of factors, including the strength of the evoked potentials as well as user comfort and safety with light stimuli flashing at those frequencies. One way to tackle this limitation is by introducing targets flickering at the same frequency but with different phases. In this paper, we propose the use of the analytic common spatial patterns (ACSPs) method to discriminate between phase coded SSVEP targets, and we demonstrate that the complex-valued spatial filters used for discrimination can exceed the performance of existing techniques. Furthermore, the ACSP method also yields a set of spatial patterns, separable into amplitude and phase components, that provide insight into the underlying brain activity.

Complex-valued spatial filters for task discrimination.

The method of common spatial patterns (CSP) has been widely adopted for the discrimination of mental tasks using EEG data. In this paper, some limitations of the standard CSP implementation when considering data where phase relationships play a significant role are highlighted. Furthermore, a variant of the CSP method based on the analytic representation of signals is proposed to make up for these drawbacks. The advantages of the proposed method over the standard CSP implementation are demonstrated using simulated data and tests with real EEG data. Specifically, it is shown that the complex-valued spatial filters and the derived spatial patterns can improve the discrimination process and give a more adequate representation of the tasks being considered, respectively.

Review on solving the inverse problem in EEG source analysis.

In this primer, we give a review of the inverse problem for EEG source localization. This is intended for the researchers new in the field to get insight in the state-of-the-art techniques used to find approximate solutions of the brain sources giving rise to a scalp potential recording. Furthermore, a review of the performance results of the different techniques is provided to compare these different inverse solutions. The authors also include the results of a Monte-Carlo analysis which they performed to compare four non parametric algorithms and hence contribute to what is presently recorded in the literature. An extensive list of references to the work of other researchers is also provided. This paper starts off with a mathematical description of the inverse problem and proceeds to discuss the two main categories of methods which were developed to solve the EEG inverse problem, mainly the non parametric and parametric methods. The main difference between the two is to whether a fixed number of dipoles is assumed a priori or not. Various techniques falling within these categories are described including minimum norm estimates and their generalizations, LORETA, sLORETA, VARETA, S-MAP, ST-MAP, Backus-Gilbert, LAURA, Shrinking LORETA FOCUSS (SLF), SSLOFO and ALF for non parametric methods and beamforming techniques, BESA, subspace techniques such as MUSIC and methods derived from it, FINES, simulated annealing and computational intelligence algorithms for parametric methods. From a review of the performance of these techniques as documented in the literature, one could conclude that in most cases the LORETA solution gives satisfactory results. In situations involving clusters of dipoles, higher resolution algorithms such as MUSIC or FINES are however preferred. Imposing reliable biophysical and psychological constraints, as done by LAURA has given superior results. The Monte-Carlo analysis performed, comparing WMN, LORETA, sLORETA and SLF, for different noise levels and different simulated source depths has shown that for single source localization, regularized sLORETA gives the best solution in terms of both localization error and ghost sources. Furthermore the computationally intensive solution given by SLF was not found to give any additional benefits under such simulated conditions.

Review on solving the forward problem in EEG source analysis.

BACKGROUND: The aim of electroencephalogram (EEG) source localization is to find the brain areas responsible for EEG waves of interest. It consists of solving forward and inverse problems. The forward problem is solved by starting from a given electrical source and calculating the potentials at the electrodes. These evaluations are necessary to solve the inverse problem which is defined as finding brain sources which are responsible for the measured potentials at the EEG electrodes. METHODS: While other reviews give an extensive summary of the both forward and inverse problem, this review article focuses on different aspects of solving the forward problem and it is intended for newcomers in this research field. RESULTS: It starts with focusing on the generators of the EEG: the post-synaptic potentials in the apical dendrites of pyramidal neurons. These cells generate an extracellular current which can be modeled by Poisson's differential equation, and Neumann and Dirichlet boundary conditions. The compartments in which these currents flow can be anisotropic (e.g. skull and white matter). In a three-shell spherical head model an analytical expression exists to solve the forward problem. During the last two decades researchers have tried to solve Poisson's equation in a realistically shaped head model obtained from 3D medical images, which requires numerical methods. The following methods are compared with each other: the boundary element method (BEM), the finite element method (FEM) and the finite difference method (FDM). In the last two methods anisotropic conducting compartments can conveniently be introduced. Then the focus will be set on the use of reciprocity in EEG source localization. It is introduced to speed up the forward calculations which are here performed for each electrode position rather than for each dipole position. Solving Poisson's equation utilizing FEM and FDM corresponds to solving a large sparse linear system. Iterative methods are required to solve these sparse linear systems. The following iterative methods are discussed: successive over-relaxation, conjugate gradients method and algebraic multigrid method. CONCLUSION: Solving the forward problem has been well documented in the past decades. In the past simplified spherical head models are used, whereas nowadays a combination of imaging modalities are used to accurately describe the geometry of the head model. Efforts have been done on realistically describing the shape of the head model, as well as the heterogenity of the tissue types and realistically determining the conductivity. However, the determination and validation of the in vivo conductivity values is still an important topic in this field. In addition, more studies have to be done on the influence of all the parameters of the head model and of the numerical techniques on the solution of the forward problem.

The independent components of auditory P300 and CNV evoked potentials derived from single-trial recordings.

The back-projected independent components (BICs) of single-trial, auditory P300 and contingent negative variation (CNV) evoked potentials (EPs) were derived using independent component analysis (ICA) and cluster analysis. The method was tested in simulation including a study of the electric dipole equivalents of the signal sources. P300 data were obtained from healthy and Alzheimer's disease (AD) subjects. The BICs were of approximately 100 ms duration and approximated positive- and negative-going half-sinusoids. Some positively and negatively peaking BICs constituting the P300 coincided with known peaks in the averaged P300. However, there were trial-to-trial differences in their occurrences, particularly where a positive or a negative BIC could occur with the same latency in different trials, a fact which would be obscured by averaging them. These variations resulted in marked differences in the shapes of the reconstructed, artefact-free, single-trial P300s. The latencies of the BIC associated with the P3b peak differed between healthy and AD subjects (p < 0.01). More reliable evidence than that obtainable from single-trial or averaged P300s is likely to be found by studying the properties of the BICs over a number of trials. For the CNV, BICs corresponding to both the orienting and the expectancy components were found.