In all patients, nasal pain was accompanied by ictal semiological features, such as autonomic, olfactory, abdominal, or psychic auras, and focal impaired awareness seizures, which are typically associated with mesial temporal lobe epilepsy.

Our findings suggest that nasal pain can occur as an epileptic aura in patients with temporal lobe epilepsy with probable involvement of the amygdala.

Our findings suggest that nasal pain can occur as an epileptic aura in patients with temporal lobe epilepsy with probable involvement of the amygdala.

Our objective is to describe the most prevalent electroencephalographic findings in COVID-19 hospitalized patients, and to determine possible predictors of mortality including EEG and clinical variables.

A multicentric prospective observational study in patients with COVID-19 requiring EEG during hospitalization.

We found 94 EEG from 62 patients (55 % men, mean age 59.7 ± 17.8 years) were analyzed. Most frequent comorbidity was cardiac (52 %), followed by metabolic (45 %) and CNS disease (39 %). Patients required ICU management by 60 %, with a mortality of 27 % in the whole cohort. The most frequent EEG finding was generalized continuous slow-wave activity (66 %). Epileptic activity was observed in 19 % including non-convulsive status epilepticus, seizures and interictal epileptiform discharges. Periodic patterns were observed in 3 patients (3.2 %). Multivariate analysis found that cancer comorbidity and requiring an EEG during the third week of evolution portended a higher risk of mortality CONCLUSION ality CONCLUSION We observed that the most prevalent EEG finding in this cohort was generalized continuous slow-wave activity, while epileptic activity was observed in less than 20 % of the cases. Mortality risk factors were comorbidity with cancer and requiring an EEG during the third week of evolution, possibly related to the hyperinflammatory state.

The novel coronavirus disease 2019 (COVID-19) epidemic that emerged in December 2019 has rapidly evolved in recent months to become a worldwide and ongoing pandemic. Shortage of medical masks remains an unresolved problem. This study aims to investigate the filtration efficiency (FE) of home-made masks that could be used as alternatives for community mitigation of COVID-19.

Experimental observational analytic study.

The FE of home-made masks and medical masks (as the control) were tested under laminar flow within a scaled air duct system using nebulised NaCl aerosols sized 6-220nm. The size-resolved NaCl aerosol count was measured using a scanning mobility particle-sizer spectrometer. Home-made masks with an external plastic face shield also underwent a splash test. In addition, the fibre structures of medical masks were studied under an electron microscope after treatment with either 75% alcohol or soap and water at 60°C.

The FE of the home-made masks at 6-200nm were non-inferior to that of medical mion strategies could be used to reserve medical masks for use in healthcare and certain high-risk community settings, such as symptomatic persons, caregivers and attendees to healthcare institutions.Hepatocellular Carcinoma (HCC) is one of the most common malignant tumors, and high recurrence and metastasis are the major obstacles to successful treatment of HCC. Traditional Chinese medicine has little known and unique advantages in the treatment of HCC. Previous studies have confirmed that Chinese herbal formula Qingrejiedu (clears away heat and toxins), Huoxuehuayu (promotes blood flow to remove stasis) and Fuzhengguben (strengthens healthy qi and root) (QHF) has a significant effect on patients with advanced HCC, improves the quality of life and prolongs the survival time of patients significantly. In this study, we investigated the effect of QHF on proliferation, migration and invasion of human high metastatic hepatocellular carcinoma cell line HCCLM3 and its underlying mechanism. The results from our in vitro experiments showed that QHF has the ability to inhibit the proliferation by inducing G2/M phase cell cycle arrest and induce apoptosis. Moreover, QHF can also inhibit migration and invasion of HCCLM3 cells and the expression of the p-c-Met protein in HCCLM3 cells was down-regulated. c-Met is closely related to the metastasis of HCC, then we constructed a stable transfected cell line HepG2-met with high expression of c-Met by transfection. Further study in vivo revealed that c-Met gene will promote the growth of tumors and lung metastases in nude mice, and QHF intervention can reduce tumor lung metastases by inhibiting the HGF/c-Met signaling pathway. In conclusion, our study reveals that QHF can inhibit the proliferation, migration and invasion of HCCLM3, and this effect may be related to inhibiting HGF/c-Met signaling pathway.Head lice are exclusive human parasitic blood-sucking insects. Distributed worldwide among school-age children, this parasitosis generates scalp irritation and sometimes social prejudice. Understanding how head lice detect and perceive their human hosts is crucial to control transmission. Here, we describe the sensory structures present on the mouthparts of Pediculus humanus capitis and their possible contribution to the feeding decision-making process. On the anterior zone of the clypeus around the haustellum two morphological types of sensilla, invariable in location and number, were identified fourteen short clypeus bristles (SCB) and six long clypeus bristles (LCB). During feeding these structures contact the host skin but not its blood. Located antero-dorsally on the everted haustellum and between the epipharyngeal teeth, a third sensillar type was identified about four short peg epipharyngeal (SPE) sensilla. These structures are bathed with the incoming blood, when head lice feed, so may have a gustatory role. In behavioural experiments antennectomy of lice did not interfere with feeding behaviour, suggesting that the sensory structures on the mouthparts could be involved in host assessment.Identification of runner’s performance level is critical to coaching, performance enhancement and injury prevention. Machine learning techniques have been developed to measure biomechanical parameters with body-worn inertial measurement unit (IMU) sensors. However, a robust method to classify runners is still unavailable. In this paper, we developed two models to classify running performance and predict biomechanical parameters of 30 subjects. We named the models RunNet-CNN and RunNet-MLP based on their architectures convolutional neural network (CNN) and multilayer perceptron (MLP), respectively. VX478 In addition, we examined two validation approaches, subject-wise (leave-one-subject-out) and record-wise. RunNet-MLP classified runner’s performance levels with an overall accuracy of 97.1%. Our results also showed that RunNet-CNN outperformed RunNet-MLP and gradient boosting decision tree in predicting biomechanical parameters. RunNet-CNN showed good agreement (R2 > 0.9) with the ground-truth reference on biomechanical parameters.