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Immediate resuscitation is required for any sudden cardiac arrest. To improve the survival of the patient, a device to be operated by witnesses of the event—automated external defibrillator (AED)—has been produced. The aim of this study is to analyze the way and correctness of use of automated external defibrillators placed in public spaces in Polish cities. The data analyzed (using Excel 2019 and R 3.5.3 software) are 120 cases of use of automated external defibrillators, placed in public spaces in the territory of Poland in 2008–2018. The predominant location of AED use is in public transportation facilities, and the injured party is the traveler. AED use in non-hospital settings is more common in male victims aged 50–60 years. Owners of AEDs inadequately provide information about their use. The documentation that forms the basis of the emergency medical services intervention needs to be refined. There is no mention of resuscitation performed by a witness of an event or of the use of an AED. In addition, Poland lacks the legal basis for maintaining a register of automated external defibrillators. There is a need to develop appropriate documents to determine the process of reporting by the owners of the use of AEDs in out-of-hospital conditions (OHCA).

Epilepsy is a common neurological disorder worldwide and anti-epileptic drugs (AEDs) are always the first choice for treatment. However, more than 50% of patients with epilepsy who take AEDs have reported bone abnormalities. Cytochrome P450 (CYP450) isoenzymes are induced by AEDs, especially the classical AEDs, such as benzodiazepines (BZDs), carbamazepine (CBZ), phenytoin (PT), phenobarbital (PB), and valproic acid (VPA). The induction of CYP450 isoenzymes may cause vitamin D deficiency, hypocalcemia, increased fracture risks, and altered bone turnover, leading to impaired bone mineral density (BMD). Newer AEDs, such as levetiracetam (LEV), oxcarbazepine (OXC), lamotrigine (LTG), topiramate (TPM), gabapentin (GP), and vigabatrin (VB) have broader spectra, and are safer and better tolerated than the classical AEDs. The effects of AEDs on bone health are controversial. This review focuses on the impact of AEDs on growth and bone metabolism and emphasizes the need for caution and timely withdrawal of these medications to avoid serious disabilities.

This study estimates the budgetary impact of the introduction of amino-acid formula (AAF) as first-line management in the elimination diet of infants with suspected cow milk protein allergy (CMPA) in the Kingdom of Saudi Arabia (KSA), Kuwait, and the United Arab Emirates (UAE) from a health-care payer's perspective. A global decision tree model was adapted to compare estimated costs in current practice (extensively hydrolyzed formula [eHF] or soy formula [SF] with the proposed approach of early introduction of AAF as first-line treatment of CMPA in non-breast-fed infants). Model inputs were derived from explorative literature reviews and medical experts' opinions. All costs were reported in local currency, ie, Saudi Riyal (SAR) for KSA, Kuwaiti Dinar (KWD) for Kuwait, and United Arab Emirates Dirham (AED) for the UAE. Cost savings with the early introduction of AAF were 10% (SAR 15102542) in KSA, 10% (KWD 306565) in Kuwait, 17% (AED 1842018) in the UAE government sector and 13% (AED 4232932) in the UAE private sector. The highest cost reduction was observed in the cost of soy formula (SF), with a 58% reduction both in KSA (SAR 4204540) and UAE public sector (AED 110331). A significant cost reduction in medication costs in Kuwait (37%; KWD 5630) and medical examination costs in the UAE private sector (50%; AED 1508918) was observed. Results indicated that the introduction of AAF as the first line in the management of CMPA is a cost-saving strategy for the Gulf Cooperation Council (GCC) countries-KSA, Kuwait, and UAE- from a health-care payer's perspective.

Introduction: Sudden cardiac arrest (SCA), which causes more than half of all cardiovascular related deaths, can be regarded as a common massive global public health problem. Analyzing out-of-hospital cardiac arrest (OHCA) cases, one of the key components is automatic external defibrillators (AEDs). Aim: The aim of this study was to analyze the use and distribution of AEDs in Polish public places. Materials and methods: The data were analyzed by using the Excel and R calculation programs. Results: The data represents 120 uses of automatic external defibrillators used in Polish public space in the period 2008–2018. The analysis describes 1165 locations of AEDs in Poland. It was noted that the number of uses in the period 2010–2016 fluctuated at a constant value, with a significant rise in 2017. When analyzing the time of interventions in detail the following was noted: the highest percentage of interventions was observed in April, and the lowest in November; the highest number of interventions was observed on a Friday, while the least number of interventions was observed on a Sunday; most occurred between 12:00 to 16:00, and least between 20:00 to 8:00. Conclusions: The observed growth in the number of cases of AED use in public places is associated with the approach to training, the emphasis on public access to defibrillation, and, therefore, the growth of social awareness. This study will be continued. The next analysis would include 2020–2022 and would be a comparative analysis with the current research.

Increasing evidence suggests that exposure to certain antiepileptic drugs (AEDs) during critical periods of development may induce transient or long-lasting neurodevelopmental deficits across cognitive, motor and behavioral domains. The developing nervous system may endure prolonged chronic exposure to AEDs during pregnancy (in utero) or during childhood, which can lead to neurodevelopmental defects such as congenital neural tube defects, lower IQ, language deficits, autism and ADHD. To date, valproate is the most widely recognized AED to significantly negatively affect neurodevelopment, and demonstrates greater adverse effects than any other AEDs that have been assessed. Although some AEDs appear to have low risk (i.e., lamotrigine, levetiracetam), other AEDs have been implicated in a variety of studies detailed below, and many AEDs have not been adequately assessed. The purpose of this review article is to summarize our current understanding of the neurodevelopmental effects of AEDs.

High performance of the shock advisory analysis of the electrocardiogram (ECG) during cardiopulmonary resuscitation (CPR) in out-of-hospital cardiac arrest (OHCA) is important for better management of the resuscitation protocol. It should provide fewer interruptions of chest compressions (CC) for non-shockable organized rhythms (OR) and Asystole, or prompt CC stopping for early treatment of shockable ventricular fibrillation (VF). Major disturbing factors are strong CC artifacts corrupting raw ECG, which we aimed to analyze with optimized end-to-end convolutional neural network (CNN) without pre-filtering or additional sensors. The hyperparameter random search of 1500 CNN models with 2–7 convolutional layers, 5–50 filters and 5–100 kernel sizes was done on large databases from independent OHCA interventions for training (3001 samples) and validation (2528 samples). The best model, named CNN3-CC-ECG network with three convolutional layers (filters@kernels: 5@5,25@20,50@20) presented Sensitivity Se(VF) = 89%(268/301), Specificity Sp(OR) = 91.7%(1504/1640), Sp(Asystole) = 91.1%(3325/3650) on an independent test OHCA database. CNN3-CC-ECG’s ability to effectively extract features from raw ECG signals during CPR was comprehensively demonstrated, and the dependency on the CPR corruption level in ECG was tested. We denoted a significant drop of Se(VF) = 74.2% and Sp(OR) = 84.6% in very strong CPR artifacts with a signal-to-noise ratio of SNR < −9 dB, p < 0.05. Otherwise, for strong, moderate and weak CC artifacts (SNR > −9 dB, −6 dB, −3 dB), we observed insignificant performance differences: Se(VF) = 92.5–96.3%, Sp(OR) = 93.4–95.5%, Sp(Asystole) = 92.6–94.0%, p > 0.05. Performance stability with respect to CC rate was validated. Generalizable application of the optimized computationally efficient CNN model was justified by an independent OHCA database, which to our knowledge is the largest test dataset with real-life cardiac arrest rhythms during CPR.

The manuscript focuses on structure-activity relationship studies of CNS-active compounds derived from valproic acid (VPA) that have the potential to become second-generation VPA drugs. Valproic acid is one of the four most widely prescribed antiepileptic drugs (AEDs) and is effective (and regularly approved) in migraine prophylaxis and in the treatment of bipolar disorders. Valproic acid is also currently undergoing clinical trials in cancer patients. Valproic acid is the least potent of the established AEDs and its use is limited by two rare but potentially life-threatening side effects, teratogenicity and hepatotoxicity. Because AEDs treat the symptoms (seizure) and not the cause of epilepsy, epileptic patients need to take AEDs for a long period of time. Consequently, there is a substantial need to develop better and safer AEDs. To become a successful second-generation VPA, the new drug should possess the following characteristics: broad-spectrum antiepileptic activity, better potency than VPA, lack of teratogenicity and hepatotoxicity, and a favorable pharmacokinetic profile compared with VPA including a low potential for drug interactions.

Background Out-of-hospital cardiac arrest (OHCA) affects some 275,000 individuals in Europe each year. Time from collapse to defibrillation is essential for survival. As emergency medical services (EMS) response times in Sweden have increased, novel methods are needed to facilitate early treatment. Unmanned aerial vehicles (i.e. drones) have potential to deliver automated external defibrillators (AED). The aim of this simulation study was to explore bystanders’ experience of a simulated OHCA-situation where a drone delivers an AED and how the situation is affected by having one or two bystanders onsite. Methods This explorative simulation study used a mixed methodology describing bystanders’ experiences of retrieving an AED delivered by a drone in simulated OHCA situations. Totally eight participants were divided in two groups of bystanders a) alone or b) in pairs and performed CPR on a manikin for 5 minutes after which an AED was delivered by a drone at 50 m from the location. Qualitative data from observations, interviews of participants and video recordings were analysed using content analysis alongside descriptive data on time delays during bystander interaction. Results Three categories of bystander experiences emerged: 1) technique and preparedness, 2) support through conversation with the dispatcher, and 3) aid and decision-making. The main finding was that retrieval of an AED as delivered by a drone was experienced as safe and feasible for bystanders. None of the participants hesitated to retrieve the AED; instead they experienced it positive, helpful and felt relief upon AED-drone arrival and were able to retrieve and attach the AED to a manikin. Interacting with the AED-drone was perceived as less difficult than performing CPR or handling their own mobile phone during T-CPR. Single bystander simulation introduced a significant hands-off interval when retrieving the AED, a period lasting 94 s (range 75 s–110 s) with one participant compared to 0 s with two participants. Conclusion The study shows that it made good sense for bystanders to interact with a drone in this simulated suspected OHCA. Bystanders experienced delivery of AED as safe and feasible. This has potential implications, and further studies on bystanders’ experiences in real cases of OHCA in which a drone delivers an AED are therefore necessary.

This paper proposes a method called autoencoder with probabilistic LightGBM (AED-LGB) for detecting credit card frauds. This deep learning-based AED-LGB algorithm first extracts low-dimensional feature data from high-dimensional bank credit card feature data using the characteristics of an autoencoder which has a symmetrical network structure, enhancing the ability of feature representation learning. The credit card fraud dataset comes from a real dataset anonymized by a bank and is highly imbalanced, with normal data far greater than fraud data. For this situation, the smote algorithm is used to resample the data before putting the extracted feature data into LightGBM, making the amount of fraud data and non-fraud data equal. After comparing the resampled and non-resampled data, it was found that the performance of the AED-LGB algorithm was not improved after resampling, and it was concluded that the AED-LGB algorithm is more suitable for imbalanced data. Finally, the AED-LGB algorithm is comparable with other commonly used machine learning algorithms, such as KNN and LightGBM, and it has an overall improvement of 2% in terms of the ACC index compared to LightGBM and KNN. When the threshold is set to 0.2, the MCC index of AED-LGB is 4% higher than that of the second-highest LightGBM algorithm and 30% higher than that of KNN. It shows that the AED-LGB algorithm has higher performance in accuracy, true positive rate, true negative rate, and Matthew’s correlation coefficient.

Background The use of an automated external defibrillator (AED) prior to EMS arrival can increase 30-day survival in out-of-hospital cardiac arrest (OHCA) significantly. Drones or unmanned aerial vehicles (UAV) can fly with high velocity and potentially transport devices such as AEDs to the site of OHCAs. The aim of this explorative study was to investigate the feasibility of a drone system in decreasing response time and delivering an AED. Methods Data of Global Positioning System (GPS) coordinates from historical OHCA in Stockholm County was used in a model using a Geographic Information System (GIS) to find suitable placements and visualize response times for the use of an AED equipped drone. Two different geographical models, urban and rural, were calculated using a multi-criteria evaluation (MCE) model. Test-flights with an AED were performed on these locations in rural areas. Results In total, based on 3,165 retrospective OHCAs in Stockholm County between 2006–2013, twenty locations were identified for the potential placement of a drone. In a GIS-simulated model of urban OHCA, the drone arrived before EMS in 32 % of cases, and the mean amount of time saved was 1.5 min. In rural OHCA the drone arrived before EMS in 93 % of cases with a mean amount of time saved of 19 min. In these rural locations during ( n  = 13) test flights, latch-release of the AED from low altitude (3–4 m) or landing the drone on flat ground were the safest ways to deliver an AED to the bystander and were superior to parachute release. Discussion The difference in response time for EMS between urban and rural areas is substantial, as is the possible amount of time saved using this UAV-system. However, yet another technical device needs to fit into the chain of survival. We know nothing of how productive or even counterproductive this system might be in clinical reality. Conclusions To use drones in rural areas to deliver an AED in OHCA may be safe and feasible. Suitable placement of drone systems can be designed by using GIS models. The use of an AED equipped drone may have the potential to reduce time to defibrillation in OHCA.