Syndromic Algorithms for Detection of Gambiense Human African Trypanosomiasis in South Sudan

Background

Active screening by mobile teams is considered the best method for detecting human African trypanosomiasis (HAT) caused by Trypanosoma brucei gambiense but the current funding context in many post-conflict countries limits this approach. As an alternative, non-specialist health care workers (HCWs) in peripheral health facilities could be trained to identify potential cases who need testing based on their symptoms. We explored the predictive value of syndromic referral algorithms to identify symptomatic cases of HAT among a treatment-seeking population in Nimule, South Sudan.

Methodology/Principal Findings

Symptom data from 462 patients (27 cases) presenting for a HAT test via passive screening over a 7 month period were collected to construct and evaluate over 14,000 four item syndromic algorithms considered simple enough to be used by peripheral HCWs. For comparison, algorithms developed in other settings were also tested on our data, and a panel of expert HAT clinicians were asked to make referral decisions based on the symptom dataset. The best performing algorithms consisted of three core symptoms (sleep problems, neurological problems and weight loss), with or without a history of oedema, cervical adenopathy or proximity to livestock. They had a sensitivity of 88.9–92.6%, a negative predictive value of up to 98.8% and a positive predictive value in this context of 8.4–8.7%. In terms of sensitivity, these out-performed more complex algorithms identified in other studies, as well as the expert panel. The best-performing algorithm is predicted to identify about 9/10 treatment-seeking HAT cases, though only 1/10 patients referred would test positive.

Conclusions/Significance

In the absence of regular active screening, improving referrals of HAT patients through other means is essential. Systematic use of syndromic algorithms by peripheral HCWs has the potential to increase case detection and would increase their participation in HAT programmes. The algorithms proposed here, though promising, should be validated elsewhere.     

Information filtering by synchronous spikes in a neural population

Information about time-dependent sensory stimuli is encoded by the spike trains of neurons. Here we consider a population of uncoupled but noisy neurons (each subject to some intrinsic noise) that are driven by a common broadband signal. We ask specifically how much information is encoded in the synchronous activity of the population and how this information transfer is distributed with respect to frequency bands. In order to obtain some insight into the mechanism of information filtering effects found previously in the literature, we develop a mathematical framework to calculate the coherence of the synchronous output with the common stimulus for populations of simple neuron models. Within this frame, the synchronous activity is treated as the product of filtered versions of the spike trains of a subset of neurons. We compare our results for the simple cases of (1) a Poisson neuron with a rate modulation and (2) an LIF neuron with intrinsic white current noise and a current stimulus. For the Poisson neuron, formulas are particularly simple but show only a low-pass behavior of the coherence of synchronous activity. For the LIF model, in contrast, the coherence function of the synchronous activity shows a clear peak at high frequencies, comparable to recent experimental findings. We uncover the mechanism for this shift in the maximum of the coherence and discuss some biological implications of our findings.     

The Cerebellum Optimizes Perceptual Predictions about External Sensory Events

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Measuring Information-Transfer Delays

In complex networks such as gene networks, traffic systems or brain circuits it is important to understand how long it takes for the different parts of the network to effectively influence one another. In the brain, for example, axonal delays between brain areas can amount to several tens of milliseconds, adding an intrinsic component to any timing-based processing of information. Inferring neural interaction delays is thus needed to interpret the information transfer revealed by any analysis of directed interactions across brain structures. However, a robust estimation of interaction delays from neural activity faces several challenges if modeling assumptions on interaction mechanisms are wrong or cannot be made. Here, we propose a robust estimator for neuronal interaction delays rooted in an information-theoretic framework, which allows a model-free exploration of interactions. In particular, we extend transfer entropy to account for delayed source-target interactions, while crucially retaining the conditioning on the embedded target state at the immediately previous time step. We prove that this particular extension is indeed guaranteed to identify interaction delays between two coupled systems and is the only relevant option in keeping with Wiener’s principle of causality. We demonstrate the performance of our approach in detecting interaction delays on finite data by numerical simulations of stochastic and deterministic processes, as well as on local field potential recordings. We also show the ability of the extended transfer entropy to detect the presence of multiple delays, as well as feedback loops. While evaluated on neuroscience data, we expect the estimator to be useful in other fields dealing with network dynamics.     

Monoclonal Antibody RYSK173 Recognizes the Dinuclear Zn Center of Serum Carnosinase 1 (CN-1): Possible Consequences of Zn Binding for CN-1 Recognition by RYSK173

Background and Aims

The proportion of serum carnosinase (CN-1) recognized by RYSK173 monoclonal antibody negatively correlates with CN-1 activity. We thus hypothesized that the epitope recognized by RYSK173 is accessible only in a catalytically incompetent conformation of the zinc dependent enzyme and we mapped its position in the CN-1 structure. Since patients with kidney failure are often deficient in zinc and other trace elements we also assessed the RYSK173 CN-1 proportion in serum of these patients and studied the influence of hemodialysis hereon in relation to Zn²⁺ and Cu²⁺ concentration during hemodialysis.

Methods and Results

Epitope mapping using myc-tagged CN-1 fragments and overlapping peptides revealed that the RYSK173 epitope directly contributes to the formation of the dinuclear Zn center in the catalytic domain of homodimeric CN-1. Binding of RYSK173 to CN-1 was however not influenced by addition of Zn²⁺ or Cu²⁺ to serum. In serum of healthy controls the proportion of CN-1 recognized by RYSK173 was significantly lower compared to end-stage renal disease (ESRD) patients (1.12 ± 0.17 vs. 1.56 ± 0.40% of total CN-1; p<0.001). During hemodialysis the relative proportion of RYSK173 CN-1 decreased in parallel with increased serum Zn²⁺ and Cu²⁺ concentrations after dialysis.

Conclusions

Our study clearly indicates that RYSK173 recognizes a sequence within the transition metal binding site of CN-1, thus supporting our hypothesis that metal binding to CN-1 masks the epitope. The CN-1 RYSK173 proportion appears overall increased in ESRD patients, yet it decreases during hemodialysis possibly as a consequence of a relative increase in transition metal bound enzyme.     

S-100 B Concentrations Are a Predictor of Decreased Survival in Patients with Major Trauma,Independently of Head Injury

Background

Major trauma remains one of the principle causes of disability and death throughout the world. There is currently no satisfactory risk assessment to predict mortality in patients with major trauma. The aim of our study is to examine whether S-100 B protein concentrations correlate with injury severity and survival in patients with major trauma, with special emphasis on patients without head injury.

Methods

Our retrospective data analysis comprised adult patients admitted to our emergency department between 1.12. 2008 and 31.12 2010 with a suspected major trauma. S-100 B concentrations were routinely assessed in major trauma patients.

Results

A total of 27.7% (378) of all patients had major trauma. The median ISS was 24.6 (SD 8.4); 16.6% (63/378) of the patients died. S-100 B concentrations correlated overall with the ISS (p<0.0001). Patients who died had significantly higher S-100 B concentrations than survivors (8.2 μg/l versus 2.2 μg/l, p<0.0001). Polytraumatised patients with and without head trauma did not differ significantly with respect to S-100 B concentration (3.2 μg/l (SD 5.3) versus 2.9 μg/l (SD 3.8), respectively, p = 0.63) or with respect to Injury Severity Score (24.8 (SD 8.6) versus 24.2 (SD 8.1), respectively, p = 0.56). S-100 B concentrations correlated negatively with survival (p<0.0001) in all patients and in both subgroups (p = 0.001 and p = 0.006, respectively)

Conclusions

S-100 concentrations on admission correlate positively with greater injury severity and decreased survival in major trauma patients, independently of the presence of a head injury. S-100 B protein levels at admission in patients with major trauma may therefore be used to assess outcome in all polytraumatised patients. These measurements should be subject to further evaluation.