Person:

Sanchez Fernandez, Ivan

Neuronal activity is critical for synaptogenesis and the development of neuronal networks. In the immature brain excitation predominates over inhibition facilitating the development of normal brain circuits, but also rendering it more susceptible to seizures. In this paper, we review the evolution of the subunit composition of neurotransmitter receptors during development, how it promotes excitation in the immature brain, and how this subunit composition of neurotransmission receptors may be also present in the epileptic brain. During normal brain development, excitatory glutamate receptors peak in function and gamma-aminobutiric acid (GABA) receptors are mainly excitatory rather than inhibitory. A growing body of evidence from animal models of epilepsy and status epilepticus has demonstrated that the brain exposed to repeated seizures presents a subunit composition of neurotransmitter receptors that mirrors that of the immature brain and promotes further seizures and epileptogenesis. Studies performed in samples from the epileptic human brain have also found a subunit composition pattern of neurotransmitter receptors similar to the one found in the immature brain. These findings provide a solid rationale for tailoring antiepileptic treatments to the specific subunit composition of neurotransmitter receptors and they provide potential targets for the development of antiepileptogenic treatments.

Continuous spikes and waves during sleep (CSWS) is an epileptic encephalopathy characterized in most patients by (1) difficult to control seizures, (2) interictal epileptiform activity that becomes prominent during sleep leading to an electroencephalogram (EEG) pattern of electrical status epilepticus in sleep (ESES), and (3) neurocognitive regression. In this paper, we will summarize current epidemiological, clinical, and EEG knowledge on CSWS and will provide suggestions for treatment. CSWS typically presents with seizures around 2–4 years of age. Neurocognitive regression occurs around 5-6 years of age, and it is accompanied by subacute worsening of EEG abnormalities and seizures. At approximately 6–9 years of age, there is a gradual resolution of seizures and EEG abnormalities, but the neurocognitive deficits persist in most patients. The cause of CSWS is unknown, but early developmental lesions play a major role in approximately half of the patients, and genetic associations have recently been described. High-dose benzodiazepines and corticosteroids have been successfully used to treat clinical and electroencephalographic features. Corticosteroids are often reserved for refractory disease because of adverse events. Valproate, ethosuximide, levetiracetam, sulthiame, and lamotrigine have been also used with some success. Epilepsy surgery may be considered in a few selected patients.

Background Tuberculosis is a major cause of morbidity and mortality in the developing world. Drug resistance, which is predicted to rise in many countries worldwide, threatens tuberculosis treatment and control.

Objective To identify features associated with treatment failure and to predict which patients are at highest risk of treatment failure.

Methods On a multi-country dataset managed by the National Institute of Allergy and Infectious Diseases we applied various machine learning techniques to identify factors statistically associated with treatment failure and to predict treatment failure based on baseline demographic and clinical characteristics alone.

Results The complete-case analysis database consisted of 587 patients (68% males) with a median (p25-p75) age of 40 (30–51) years. Treatment failure occurred in approximately one fourth of the patients. The features most associated with treatment failure were patterns of drug sensitivity, imaging findings, findings in the microscopy Ziehl-Nielsen stain, education status, and employment status. The most predictive model was forward stepwise selection (AUC: 0.74), although most models performed at or above AUC 0.7. A sensitivity analysis using the 643 original patients filling the missing values with multiple imputation showed similar predictive features and generally increased predictive performance.

Conclusion Machine learning can help to identify patients at higher risk of treatment failure. Closer monitoring of these patients may decrease treatment failure rates and prevent emergence of antibiotic resistance. The use of inexpensive basic demographic and clinical features makes this approach attractive in low and middle-income countries.