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PurposeCase studies on life cycle assessments frequently admit that the precision of their outcome could be undermined due to temporal issues, though they usually refrain from offering much more detail. In addition, available overview papers and reviews on problems and challenges in LCA do not address the whole range of temporal issues. As those are major sources of inaccuracies and influence each other, it is important to get a clear picture of them, to close gaps in definitions, to systemize temporal issues, and to show their interdependencies and proposed solutions. In order to identify the state of science on those questions, we conducted a systematic literature review.MethodsWe first systematized temporal issues based on ISO 14040ff and divided them into six types: time horizon, discounting, temporal resolution of the inventory, time-dependent characterization, dynamic weighting, and time-dependent normalization. Building on that, we identified suitable search terms and developed an analysis grid for the content analysis. We included only methodological papers and case studies with original findings on solutions for temporal issues. Bibliographic data, impact types, industrial fields, and methodological contributions were analyzed.Results and discussionLiterature differentiates between different types of time horizons. There is one for the whole assessment, defined in goal and scope, one for the life cycle inventory, and one for the impact characterization. Setting a time horizon for the assessment is regarded as equivalent to the application of discounting. Both very long and very short time horizons of the assessment are not practical depending on the topic assessed in the LCA. Very short ones would offend the principle of intergenerational equality, while very long ones would marginalize short-term actions and thereby reduce incentives to act. There is consensus in the literature that temporally differentiated life cycle inventories and time-dependent, or at least time horizon dependent, characterization improve the accuracy of LCA. Generally, dynamic life cycle assessments are attractive for companies because the calculation results are not only more accurate but are often also lower than in static life cycle assessments.ConclusionThe main questions where we did not find consensus are the issue of the length of the time horizon of the assessment and the issue of discounting. Those are regarded as subjective and are encountered with sensitivity or scenario analysis. Further investigations should be taken for a better understanding of this issue and for concrete solutions because their influence on the results of life cycle assessments is often fundamental.

Electroencephalogram (EEG) datasets from epilepsy patients have been used to develop seizure detection and prediction algorithms using machine learning (ML) techniques with the aim of implementing the learned model in a device. However, the format and structure of publicly available datasets are different from each other, and there is a lack of guidelines on the use of these datasets. This impacts the generatability, generalizability, and reproducibility of the results and findings produced by the studies. In this narrative review, we compiled and compared the different characteristics of the publicly available EEG datasets that are commonly used to develop seizure detection and prediction algorithms. We investigated the advantages and limitations of the characteristics of the EEG datasets. Based on our study, we identified 17 characteristics that make the EEG datasets unique from each other. We also briefly looked into how certain characteristics of the publicly available datasets affect the performance and outcome of a study, as well as the influences it has on the choice of ML techniques and preprocessing steps required to develop seizure detection and prediction algorithms. In conclusion, this study provides a guideline on the choice of publicly available EEG datasets to both clinicians and scientists working to develop a reproducible, generalizable, and effective seizure detection and prediction algorithm.

Despite the availability of over 30 antiseizure medications (ASMs), there is no “one size fits it all,” so there is a continuing search for novel ASMs. There are divergent data demonstrating that modulation of distinct serotonin (5‐hydroxytryptamine, 5‐HT) receptors subtypes could be beneficial in the treatment of epilepsy and its comorbidities, whereas only a few ASM, such as fenfluramine (FA), act via 5‐HT. There are 14 different 5‐HT receptor subtypes, and most epilepsy studies focus on one or a few of these subtypes, using different animal models and different ligands. We reviewed the available evidence of each 5‐HT receptor subtype using MEDLINE up to July 2021. Our search included medical subject heading (MeSH) and free terms of each “5‐HT subtype” separately and its relation to “epilepsy or seizures.” Most research underlines the antiseizure activity of 5‐HT1A,1D,2A,2C,3 agonism and 5‐HT6 antagonism. Consistently, FA, which has recently been approved for the treatment of seizures in Dravet syndrome, is an agonist of 5‐HT1D,2A,2C receptors. Even though each study focused on a distinct seizure/epilepsy type and generalization of different findings could lead to false interpretations, we believe that the available preclinical and clinical studies emphasize the role of serotonergic modulation, especially stimulation, as a promising avenue in epilepsy treatment.

Epilepsy is the second most prevalent neurological disease worldwide. It is mainly characterized by an electrical abnormal activity in different brain regions. The massive entrance of Ca2+ into neurons is the main neurotoxic process that lead to cell death and finally to neurodegeneration. Although there are a huge number of antiseizure medications, there are many patients who do not respond to the treatments and present refractory epilepsy. In this context, nanomedicine constitutes a promising alternative to enhance the central nervous system bioavailability of antiseizure medications. The encapsulation of different chemical compounds at once in a variety of controlled drug delivery systems gives rise to an enhanced drug effectiveness mainly due to their targeting and penetration into the deepest brain region and the protection of the drug chemical structure. Thus, in this review we will explore the recent advances in the development of drugs associated with polymeric and lipid‐based nanocarriers as novel tools for the management of epilepsy disorders.

Epilepsy is one of the most common serious chronic neurological diseases affecting people of all ages globally. It is characterized by recurrent seizures. About 50 million people worldwide have epilepsy. Indubitably, people with epilepsy (PWE) may be without access to appropriate treatment. Many studies have examined the molecular mechanisms and clinical aspects of epilepsy; nonetheless, the treatment gap exists in some special areas. In the tropics, the specific geographical and ecological conditions and a lack of medical resources result in neglect or delay of diagnosis for PWE. Herein, we summarized the epidemiology of epilepsy in the tropics and discussed the disease burden and existing problems, aiming to offer a medical environment for patients in need and highlight the importance of reducing the epileptic disease burden in tropical countries.

Myoclonus classically presents as a brief (10–50 ms duration), non‐rhythmic jerk movement. The etiology could vary considerably ranging from self‐limited to chronic or even progressive disorders, the latter falling into encephalopathic pictures that need a prompt diagnosis. Beyond the etiological classification, others evaluate myoclonus' body distribution (i.e., clinical classification) or the location of the generator (i.e., neurophysiological classification); particularly, knowing the anatomical source of myoclonus gives inputs on the observable clinical patterns, such as EMG bursts duration or EEG correlate, and guides the therapeutic choices. Among all the chronic disorders, myoclonus often presents itself as a manifestation of epilepsy. In this context, myoclonus has many facets. Myoclonus occurs as one, or the only, seizure manifestation while it can also present as a peculiar type of movement disorder; moreover, its electroclinical features within specific genetically determined epileptic syndromes have seldom been investigated. In this review, following a meeting of recognized experts, we provide an up‐to‐date overview of the neurophysiology and nosology surrounding myoclonus. Through the dedicated exploration of epileptic syndromes, coupled with pragmatic guidance, we aim to furnish clinicians and researchers alike with practical advice for heightened diagnostic management and refined treatment strategies. Plain Language Summary In this work, we described myoclonus, a movement characterized by brief, shock‐like jerks. Myoclonus could be present in different diseases and its correct diagnosis helps treatment.

Post‐traumatic epilepsy ( PTE ) is a relatively underappreciated condition that can develop as a secondary consequence following traumatic brain injury ( TBI ). The aim of this rapid evidence review is to provide a synthesis of existing evidence on the effectiveness of treatment interventions for the prevention of PTE in people who have suffered a moderate/severe TBI to increase awareness and understanding among consumers. Electronic medical databases (n = 5) and gray literature published between January 2010 and April 2015 were searched for studies on the management of PTE . Twenty‐two eligible studies were identified that met the inclusion criteria. No evidence was found for the effectiveness of any pharmacological treatments in the prevention or treatment of symptomatic seizures in adults with PTE . However, limited high‐level evidence for the effectiveness of the antiepileptic drug levetiracetam was identified for PTE in children. Low‐level evidence was identified for nonpharmacological interventions in significantly reducing seizures in patients with PTE , but only in a minority of cases, requiring further high‐level studies to confirm the results. This review provides an opportunity for researchers and health service professionals to better understand the underlying pathophysiology of PTE to develop novel, more effective therapeutic targets and to improve the quality of life of people with this condition.

More than one‐third of people with epilepsy develop drug‐resistant epilepsy (DRE). Different hypotheses have been proposed to explain the origin of DRE. Accumulating evidence suggests the contribution of neuroinflammation, modifications in the integrity of the blood‐brain barrier (BBB), and altered immune responses in the pathophysiology of DRE. The inflammatory response is mainly due to the increase of cytokines and related molecules; these molecules have neuromodulatory effects that contribute to hyperexcitability in neural networks that cause seizure generation. Some patients with DRE display the presence of autoantibodies in the serum and mainly cerebrospinal fluid. These patients are refractory to the different treatments with standard antiseizure medications (ASMs), and they could be responding well to immunomodulatory therapies. This observation emphasizes that the etiopathogenesis of DRE is involved with immunology responses and associated long‐term events and chronic inflammation processes. Furthermore, multiple studies have shown that functional polymorphisms as risk factors are involved in inflammation processes. Several relevant polymorphisms could be considered risk factors involved in inflammation‐related DRE such as receptor for advanced glycation end products (RAGE) and interleukin 1β (IL‐1β). All these evidences sustained the hypothesis that the chronic inflammation process is associated with the DRE. However, the effect of the chronic inflammation process should be investigated in further clinical studies to promote the development of novel therapeutics useful in treatment of DRE.

A reliable seizure detection or prediction device can potentially reduce the morbidity and mortality associated with epileptic seizures. Previous findings indicating alterations in cardiac activity during seizures suggest the usefulness of cardiac parameters for seizure detection or prediction. This study aims to examine available studies on seizure detection and prediction based on cardiac parameters using non-invasive wearable devices. The Embase, PubMed, and Scopus databases were used to systematically search according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Human studies that evaluated seizure detection or prediction based on cardiac parameters collected using wearable devices were included. The QUADAS-2 tool and proposed standards for validation for seizure detection devices were used for quality assessment. Twenty-four articles were identified and included in the analysis. Twenty studies evaluated seizure detection algorithms, and four studies focused on seizure prediction. Most studies used either a wrist-worn or chest-worn device for data acquisition. Among the seizure detection studies, cardiac parameters utilized for the algorithms mainly included heart rate (HR) (n = 11) or a combination of HR and heart rate variability (HRV) (n = 6). HR-based seizure detection studies collectively reported a sensitivity range of 56%-100% and a false alarm rate (FAR) of 0.02-8/h, with most studies performing retrospective validation of the algorithms. Three of the seizure prediction studies retrospectively validated multimodal algorithms, combining cardiac features with other physiological signals. Only one study prospectively validated their seizure prediction algorithm using HRV extracted from ECG data collected from a custom wearable device. These studies have demonstrated the feasibility of using cardiac parameters for seizure detection and prediction with wearable devices, with varying algorithmic performance. Many studies are in the proof-of-principle stage, and evidence for real-time detection or prediction is currently limited. Future studies should prioritize further refinement of the algorithm performance with prospective validation using large-scale longitudinal data. PLAIN LANGUAGE SUMMARY: This systematic review highlights the potential use of wearable devices, like wristbands, for detecting and predicting seizures via the measurement of heart activity. By reviewing 24 articles, it was found that most studies focused on using heart rate and changes in heart rate for seizure detection. There was a lack of studies looking at seizure prediction. The results were promising but most studies were not conducted in real-time. Therefore, more real-time studies are needed to verify the usage of heart activity-related wearable devices to detect seizures and even predict them, which will be beneficial to people with epilepsy.

Drug‐resistant epilepsy remains to this day as a highly prevalent condition affecting around one‐third of patients with epilepsy, despite all the research and the development of several new antiseizure medications (ASMs) over the last decades. Epilepsies are multifactorial complex diseases, commonly associated with psychiatric, neurological, and somatic comorbidities. Thus, to solve the puzzling problem of pharmacoresistance, the diagnosis and modeling of epilepsy and comorbidities need to change toward a complex system approach. In this review, we have summarized the sequence of events for the definition of epilepsies and comorbidities, the search for mechanisms, and the major hypotheses of pharmacoresistance, drawing attention to some of the many converging aspects between the proposed mechanisms, their supporting evidence, and comorbidities‐related alterations. The use of systems biology applied to epileptology may lead to the discovery of new targets and the development of new ASMs, as may advance our understanding of the epilepsies and their comorbidities, providing much deeper insight on multidrug pharmacoresistance.