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Clinical studies found that medication for attention-deficit/hyperactivity disorder (ADHD) is effective in coexisting autism spectrum disorder (ASD), but current research is based on small clinical studies mainly performed on children or adolescents. We here use register data to examine if individuals with ADHD and coexisting ASD present differences in the prescribing patterns of ADHD medication when compared to individuals with pure ADHD.

Data with information on filled prescriptions and diagnoses was retrieved from the Swedish Prescribed Drug Register and the National Patient Register. We identified 34,374 individuals with pure ADHD and 5012 individuals with ADHD and coexisting ASD, aged between 3 and 80 years. The first treatment episode with ADHD medications (≥ 2 filled prescriptions within 90 days) and daily doses of methylphenidate during a 3-year period was measured. Odds ratios (ORs) were calculated for the likelihood of being prescribed ADHD medication in individuals with and without ASD and Wilcls with or without ASD. If these differences are due to different medication responses in ASD or due to other factors such as clinicians' perceptions of medication effects in patients with ASD, needs to be further studied.

Sudden death in epilepsy (SUDEP) is a rare disease in US, however, they account for 8-17% of deaths in people with epilepsy. This disease involves complicated physiological patterns and it is still not clear what are the physio-/bio-makers that can be used as an indicator to predict SUDEP so that care providers can intervene and treat patients in a timely manner. For this sake, UTHealth School of Biomedical Informatics (SBMI) organized a machine learning Hackathon to call for advanced solutions https//sbmi.uth.edu/hackathon/archive/sept19.htm .

In recent years, deep learning has become state of the art for many domains with large amounts data. Although healthcare has accumulated a lot of data, they are often not abundant enough for subpopulation studies where deep learning could be beneficial. Taking these limitations into account, we present a framework to apply deep learning to the detection of the onset of slow activity after a generalized tonic-clonic seizure, as well as other EEG signal detection problems exhibiting data paucity.

We conducted ten training runs for our full method and seven model variants, statistically demonstrating the impact of each technique used in our framework with a high degree of confidence.

Our findings point toward deep learning being a viable method for detection of the onset of slow activity provided approperiate regularization is performed.

Our findings point toward deep learning being a viable method for detection of the onset of slow activity provided approperiate regularization is performed.

Sudden Unexpected Death in Epilepsy (SUDEP) has increased in awareness considerably over the last two decades and is acknowledged as a serious problem in epilepsy. However, the scientific community remains unclear on the reason or possible bio markers that can discern potentially fatal seizures from other non-fatal seizures. The duration of postictal generalized EEG suppression (PGES) is a promising candidate to aid in identifying SUDEP risk. The length of time a patient experiences PGES after a seizure may be used to infer the risk a patient may have of SUDEP later in life. However, the problem becomes identifying the duration, or marking the end, of PGES (Tomson et al. in Lancet Neurol 7(11)1021-1031, 2008; Nashef in Epilepsia 386-8, 1997).

This work addresses the problem of marking the end to PGES in EEG data, extracted from patients during a clinically supervised seizure. This work proposes a sensitivity analysis on EEG window size/delay, feature extraction and classifiers along with associated hyperps to be able to predict a patient's SUDEP risk.

In recent decades, the prevalence of chronic diseases in children and adolescents has increased significantly. Contextual factors play a central role in the self-regulation of chronic diseases. They influence illness and treatment representations, disease management, and health outcomes. While previous studies have investigated the influence of contextual factors on children's beliefs about their illness, little is known about subjective contextual factors of treatment representations of children and adolescents with chronic diseases, especially in the context of rehabilitation. Therefore, the aim of this qualitative analysis was to examine the contextual factors reported by chronically ill children and adolescents in relation to their treatment representations. Furthermore, we aimed to assign the identified themes to classifications of environmental and personal contextual factors in the context of the International Classification of Functioning, Disability and Health (ICF).

Between July and September 20ontextual factors have an important impact on self-regulation, little attention is paid to their investigation. Personal and environmental factors probably influence patients' treatment representations in terms of expectations and concerns as well as emotions regarding the treatment. Considering contextual factors could lead to the more appropriate allocation of medical care and the better customisation of treatment.

Although contextual factors have an important impact on self-regulation, little attention is paid to their investigation. Personal and environmental factors probably influence patients' treatment representations in terms of expectations and concerns as well as emotions regarding the treatment. Considering contextual factors could lead to the more appropriate allocation of medical care and the better customisation of treatment.

Sudden unexpected death in epilepsy (SUDEP) is a leading cause of premature death in patients with epilepsy. If timely assessment of SUDEP risk can be made, early interventions for optimized treatments might be provided. One of the biomarkers being investigated for SUDEP risk assessment is postictal generalized EEG suppression [postictal generalized EEG suppression (PGES)]. For example, prolonged PGES has been found to be associated with a higher risk for SUDEP. Accurate characterization of PGES requires correct identification of the end of PGES, which is often complicated due to signal noise and artifacts, and has been reported to be a difficult task even for trained clinical professionals. In this work we present a method for automatic detection of the end of PGES using multi-channel EEG recordings, thus enabling the downstream task of SUDEP risk assessment by PGES characterization.

We address the detection of the end of PGES as a classification problem. Given a short EEG snippet, a trained model classition for the detection of the end of PGES.

Accurate detection of the end of PGES is important for PGES characterization and SUDEP risk assessment. In this work, we showed that it is feasible to automatically detect the end of PGES-otherwise difficult to detect due to EEG noise and artifacts-using time-series features derived from multi-channel EEG recordings. In future work, we will explore deep learning based models for improved detection and investigate the downstream task of PGES characterization for SUDEP risk assessment.

Accurate detection of the end of PGES is important for PGES characterization and SUDEP risk assessment. In this work, we showed that it is feasible to automatically detect the end of PGES-otherwise difficult to detect due to EEG noise and artifacts-using time-series features derived from multi-channel EEG recordings. In future work, we will explore deep learning based models for improved detection and investigate the downstream task of PGES characterization for SUDEP risk assessment.

Rhinoviruses and influenza viruses cause millions of acute respiratory infections annually. Symptoms of mild acute respiratory infections are commonly treated with over-the-counter products like ambroxol, bromhexine, and N-acetyl cysteine, as well as of thyme and pelargonium extracts today. Because the direct antiviral activity of these over-the-counter products has not been studied in a systematic way, the current study aimed to compare their inhibitory effect against rhinovirus and influenza virus replication in an in vitro setting.

The cytotoxicity of ambroxol, bromhexine, and N-acetyl cysteine, as well as of thyme and pelargonium extracts was analyzed in Madin Darby canine kidney (MDCK) and HeLa Ohio cells. The antiviral effect of these over-the-counter products was compared by analyzing the dose-dependent inhibition (i) of rhinovirus A2- and B14-induced cytopathic effect in HeLa Ohio cells and (ii) of influenza virus A/Hong Kong/68 (subtype H3N2)- and A/Jena/8178/09 (subtype H1N1, pandemic)-induced cteps only. The proven block of viral neuraminidase activity might explain the inhibition of influenza virus replication when added after viral adsorption.

The study results indicate a distinct inhibition of influenza A virus replication by thyme and pelargonium extract which might contribute to the beneficial effects of these plant extracts on acute respiratory infections symptoms.

The study results indicate a distinct inhibition of influenza A virus replication by thyme and pelargonium extract which might contribute to the beneficial effects of these plant extracts on acute respiratory infections symptoms.The incidence and mortality of COVID-19, according to the World Health Organization reports, shows a noticeable difference between North America, Western Europe, and South Asia on one hand and most African countries on the other hand, especially the malaria-endemic countries. Although this observation could be attributed to limited testing capacity, mitigation tools adopted and cultural habits, many theories have been postulated to explain this difference in prevalence and mortality. Because death tends to occur more in elders, both the role of demography, and how the age structure of a population may contribute to the difference in mortality rate between countries were discussed. The variable distribution of the ACEI/D and the ACE2 (C1173T substitution) polymorphisms has been postulated to explain this variable prevalence. Up-to-date data regarding the role of hydroxychloroquine (HCQ) and chloroquine (CQ) in COVID-19 have been summarized. The article also sheds lights on how the similarity of malaria and COVID-19 symptoms can lead to misdiagnosis of one disease for the other or overlooking the possibility of co-infection. As the COVID-19 pandemic threatens the delivery of malaria services, such as the distribution of insecticide-treated nets (ITNs), indoor residual spraying, as well as malaria chemoprevention there is an urgent need for rapid and effective responses to avoid malaria outbreaks.

It had been shown that High-flow nasal cannula (HFNC) is an effective initial support strategy for patients with acute respiratory failure. However, the efficacy of HFNC for patients with COVID-19 has not been established. This study was performed to assess the efficacy of HFNC for patients with COVID-19 and describe early predictors of HFNC treatment success in order to develop a prediction tool that accurately identifies the need for upgrade respiratory support therapy.

We retrospectively reviewed the medical records of patients with COVID-19 treated by HFNC in respiratory wards of 2 hospitals in Wuhan between 1 January and 1 March 2020. Overall clinical outcomes, the success rate of HFNC strategy and related respiratory variables were evaluated.

A total of 105 patients were analyzed. Of these, 65 patients (61.9%) showed improved oxygenation and were successfully withdrawn from HFNC. The PaO

/FiO

ratio, SpO

/FiO

ratio and ROX index (SpO

/FiO

*RR) at 6h, 12h and 24h of HFNC initiation were closely related to the prognosis.