We spend one third of our life in sleep. The interesting point about the sleep is that the neurons are not quiescent during sleeping and they show synchronous oscillations at different regions. Especially sharp wave ripples are observed in the hippocampus. Here, we propose a simple phenomenological neural mass model for the CA1-CA3 network of the hippocampus considering the spike frequency adaptation for excitatory neurons. The model consists of one group of identical CA1 excitatory neurons, one group of identical CA1 inhibitory neurons, one group of identical CA3 excitatory neurons, and one group of identical CA3 inhibitory neurons. All the recurrent connections between the neurons of CA3 network are considered. For CA1 neurons the excitatory to inhibitory, inhibitory to excitatory and inhibitory to inhibitory connections are considered. CA1 and CA3 neurons are connected by long-range connections from CA3 excitatory neurons to both CA1 excitatory and inhibitory neurons. We show that this simple model can spontaneously generate the oscillations similar to the sharp waves in the CA3 network. The duration of the sharp waves is determined by the slow dynamic of the adaptation process. The excitatory inputs from CA3 network to the CA1 network during these sharp waves induce ripples in the CA1 network due to the interaction of excitatory and inhibitory neurons. We next show that contrary to intuition and in a very good agreement with the recent experimental findings, reduction of the excitation increases the amplitude of the ripples while decreases the frequency of them. This model can also spontaneously generate ripple doublets. The decrease in the excitation is associated with the increase in the probability of observing ripple doublets. Our results shed light on our understanding of the mechanism underlying the generation of sharp wave ripples.

Introduction: Usually osseointegration takes between three to six months after implant placement but patients are interested to have early loading. There are no definitive criteria for measuring bone mineral density (BMD), insertion torque (IT) (final torque force) and resonance frequency analysis (RFA) (primary implant stability) to determine exact loading time based on the relationship between the above-mentioned parameters. The aim of this study was to determine the relationship between IT, RFA and BMD in screw-type implants.Materials and Methods: This clinical trial was conducted on 18 patients who were candidates for ITI implant placement. Written consent was taken and jaw bone density was determined via a digital radiography technique before surgery. After implant placement, RFA and IT were measured. Fifty-five ITI implants of the total 62 implants placed were evaluated; the implants were 12 mm long with a diameter of 4.1 mm. Data was analyzed with Pearson’s test using SPSS.15 software (a=0.05).Results: There was a significant relationship between IT, RFA and BMD. Pearson’s test showed a correlation coefficient of 0.872 to 0.789 between the three parameters, indicating a strong relationship between them. The mean bone density was 1.468±0.042 g/cm2; the mean RFA was 66.01±2.2 ISQ and the mean IT was 34.62±3.33 N/cm2.Conclusion: Based on the results of the present study there is a significant relationship between, IT, RFA and BMD (p value=0.001).

Pandemic coronavirus causes respiratory, enteric and sometimes neurological diseases. Proteome data of individual coronavirus strains were already reported. Here we investigated of SARS-CoV-2 ssRNA and protein of spike, envelope and membrane to determine stress adaptation profile. Thermodynamic properties, Physicochemical behaviour and, amino acid composition along with their RMSD value was analysed. Thermodynamic index of SARS-CoV2 spike, envelope and membrane ssRNA is unstable in higher temperature. Presence of higher proportion of polar with positive and negative charged amino acid residues into spike (S), envelope (E) and membrane (M) protein indicate the lower stress adaptability pattern. Our study represented several unstable pockets into S, E and M proteins of SARS-CoV-2 against different abiotic stresses, specifically higher in spike protein. Contact with heat through solvent may denature the architectural network of SARS-CoV-2 spike, envelope and membrane ssRNA and structural protein. The stress instability index of SARS-CoV-2 and the interactome profile of its transmembrane proteins may help to reveal novel factors for inhibiting SARS-CoV-2 growth.

Background & Aims: Antidepressants are very common therapeutic agents in medicine. Design and using new antidepressants have built a new scope for depression treatment. Bupropion is an atypical antidepressant but it is used as an anti-smoke agent widely due to its smoke cessation. Its effectiveness in producing smoking cessation seems independent of its antidepressant effects. The study of synaptic effects of bupropion can reveal its mechanism for nicotine dependence cessation. This study has tested the effects of bupropion on evoked responses of rat hippocampal slices.Materials & Methods: 16 hippocampal slices from 18-25 old age rat pops were tested. The hippocampal slices have been prepared according to standard techniques. Bupropion was perfused in ACSF (2 mmol) for 30 minutes. The amplitude of population spikes (PS) amplitude of Stratum Radiatum was measured before and after bupropion perfusion. Amplitude of PS before bupropion perfusion fitted as 100% for baseline.Results: Bupropion (2 mmol) abolished PS of all slices for different periods. PS disappeared in 37.5% of slices for 1 hour, 25% for 1.5 hour, 12.5 % for 2 hour, and 25% for 4 hour or more. Bupropion disappeared PS 5-15 minutes after perfusion.Conclusion: Bupropion abolished or decreased PS amplitude. The results have showed that bupropion can decreases LTP or synaptic plasticity and alters the synaptic behavior in drug dependence.

Background: Sepsis is one of the most important causes of death in infants. The pattern of bacterial agents responsible for neonatal septicemia changes over time. The main aim of the present study was to provide a clinical guideline adapted for treatment of neonatal sepsis based on the frequency of microbial agents in the Neonatal Intensive Care Unit of Alzahra Hospital, Tabriz, Iran. Methods: The clinical guideline adaptation is conducted based on the ADAPTE Resource Toolkit for Guideline Adaptation (version 2. 0) from December 2016 to January 2018. For data collection, the specialized websites were identified, then an internet search method was used for gaining clinical guidelines and medical literature databases. A panel was established with members of multi-specialties and the obtained guidelines were examined and evaluated. In the end, the final guideline was selected and translated. Results: Regarding the guideline, employing antibiotics should start when the neonate is < 35 weeks and premature rupture of membrane (PROM) happened < 18 h. Moreover, it could be employed when the neonate did not receive antibiotics, the gestational age (GA) is < 35 weeks with a PROM < 18 h or a GA < 37 weeks with a PROM ≥ 18 h. Conclusion: Implementation of the neonatal sepsis treatment guideline leads to a unified method of treatment, reduces the risk of antibiotic resistance, and decreases the mortality and morbidity associated with sepsis.

This paper presents a new time-frequency based EEG seizure detection method. This method uses the distribution of interspike intervals as a criterion for discriminating between seizure and nonseizure activities. To detect spikes in the EEG, the signal is mapped into the time-frequency domain. The high instantaneous energy of spikes is reflected as a localized energy in time-frequency domain. Histogram of successive spikes intervals is then used as a feature for seizure detection. In the presented technique the EEG data are segmented into 4-second epochs. A k-nearest neigbor algorithm is employed to classify the EEG epochs into seizure and nonseizure groups. The performance of the presented technique is evaluated using the EEG data of five neonates. The results indicate that the proposed technique is superior to the other existing methods with 92.4% good detection rate and 4.9 % false detection rate.