Entropy in scalp EEG can be used as a preimplantation marker for VNS efficacy.

Vagus nerve stimulation (VNS) is a therapeutic option in drug-resistant epilepsy. VNS leads to ≥ 50% seizure reduction in 50 to 60% of patients, termed "responders". The remaining 40 to 50% of patients, "non-responders", exhibit seizure reduction < 50%. Our work aims to differentiate between these two patient groups in preimplantation EEG analysis by employing several Entropy methods. We identified 59 drug-resistant epilepsy patients treated with VNS. We established their response to VNS in terms of responders and non-responders. A preimplantation EEG with eyes open/closed, photic stimulation, and hyperventilation was found for each patient. The EEG was segmented into eight time intervals within four standard frequency bands. In all, 32 EEG segments were obtained. Seven Entropy methods were calculated for all segments. Subsequently, VNS responders and non-responders were compared using individual Entropy methods. VNS responders and non-responders differed significantly in all Entropy methods except Approximate Entropy. Spectral Entropy revealed the highest number of EEG segments differentiating between responders and non-responders. The most useful frequency band distinguishing responders and non-responders was the alpha frequency, and the most helpful time interval was hyperventilation and rest 4 (the end of EEG recording).

Pre-implant Heart Activity Differs in Responders and Non-responders to Vagal Nerve Stimulation Therapy in Epileptic Patients.

Vagal Nerve Stimulation (VNS) is used to treat patients with pharmacoresistant epilepsy. However, generally accepted tools to predict VNS response do not exist. Here we examined two heart activity measures - mean RR and pNN50 and their complex behavior during activation in pre-implant measurements. The ECG recordings of 73 patients (38 responders, 36 non-responders) were examined in a 30-sec floating window before (120 sec), during (2x120 sec), and after (120 sec) the hyperventilation by nose and mouth. The VNS response differentiation by pNN50 was significant (min p=0.01) in the hyperventilation by a nose with a noticeable descendant trend in nominal values. The mean RR was significant (p=0.01) in the rest after the hyperventilation by mouth but after an approximately 40-sec delay.Clinical Relevance- Our study shows that pNN50 and mean RR can be used to distinguish between VNS responders and non-responders. However, details of dynamic behavior showed how this ability varies in tested measurement segments.

Response to Vagal Stimulation by Heart-rate Features in Drug-resistant Epileptic Patients.

Vagal Nerve Stimulation (VNS) is an option in the treatment of drug-resistant epilepsy. However, approximately a quarter of VNS subjects does not respond to the therapy. In this retrospective study, we introduce heart-rate features to distinguish VNS responders and non-responders. Standard pre-implantation measurements of 66 patients were segmented in relation to specific stimuli (open/close eyes, photic stimulation, hyperventilation, and rests between). Median interbeat intervals were found for each segment and normalized (NMRR). Five NMRRs were significant; the strongest feature achieved significance with p=0.013 and AUC=0.66. Low mutual correlation and independence on EEG signals mean that presented features could be considered as an addition for models predicting VNS response using EEG.

First Report of Basil Downy Mildew Caused by Peronospora belbahrii in the Czech Republic.

Sweet basil (Ocimum basilicum L.) is an annual aromatic and medicinal plant in the Lamiaceae that is originally native to India but is grown in warm regions all over the world. It is a popular culinary herb used fresh and dried, and is used in traditional folk medicine. In the Czech Republic, sweet basil is grown commercially in South Moravia or by home gardeners as a potted plant. In 2012, severe downy mildew was observed in a field of basil plants (cv. Dark Green) at the Crop Research Institute (CRI) in Olomouc, Czech Republic. Infected leaves each exhibited large, interveinal, chlorotic lesions, and violet-gray, fuzzy growth on the lower leaf surface. Within a few days, lesions turned necrotic and severely infected leaves dropped prematurely. Microscopic observations revealed hyaline conidiophores typical of Peronospora Corda, emerging from stomata. Conidiophores (n = 100) were usually 239.9 to 296.5 × 8.7 to 10.6 µm, straight, and were branched 4 or 5 times submonopodially at the upper ends. Ultimate branchlets (n = 100) were slightly curved and obtuse, with the longer branchlets usually 17.8 to 22.7 µm and the shorter branchlets 10.0 to 12.9 µm, and each bearing a single conidium. Conidia (n = 100) were olive-brown, mostly ellipsoidal to subglobose, and typically 29.0 to 31.0 × 23.2 to 25.4 µm, with a length/width ratio of 1.2 to 1.3. Oospores were not observed. Based on these morphological characteristics, the pathogen was identified as Peronospora belbahrii Thines (5). The specimen was deposited in a local herbarium at the CRI in Olomouc, as voucher PB-1. Genomic DNA was extracted from conidia, and the internal transcribed spacer (ITS) region of ribosomal DNA (rDNA) amplified with primers DC-6 (1) and LR-0 (4). A sequence was deposited in the NCBI database (GenBank Accession No. KJ960193). A BLAST search of the NCBI database revealed 99% identity to the deposited ITS sequences of P. belbahrii from basil and other host species (EU863410, FJ394334-7, GQ390794, GQ390795, HM462241, HM462242, HM486901, HQ702191, HQ730979, KC756923, KF419289, and KF419290). P. belbahrii was first described by Thines et al. (5) as a pathogen of sweet basil and coleus (Solenostemon scutellarioides), but can also infect Agastache spp. (2). There are many reports indicating the pathogen is spreading throughout the world (5). In Europe, chronologically, basil downy mildew has been reported from Italy, France, Switzerland, Germany, Hungary, and Cyprus (2,3,5). To our knowledge, this is the first report of natural occurrence of downy mildew on sweet basil in the Czech Republic. References: (1) D. E. L. Cooke et al. Fung. Genet. Biol. 30:17, 2000. (2) D. F. Farr and A. Y. Rossman. Fungal Databases. Systematic Mycology and Microbiology Laboratory, USDA ARS. Retrieved from http://nt.ars-grin.gov/fungaldatabases/ , 16 June 2014. (3) A. Garibaldi et al. Plant Dis. 89:683, 2005. (4) O. Spring et al. Eur. J. Plant Pathol. 114:309, 2006. (5) M. Thines et al. Mycol. Res. 113:532, 2009.