Seizure outcomes of large volume temporo-parieto-occipital and frontal surgery in children with drug-resistant epilepsy.

OBJECTIVE: In this study, we investigate the seizure outcomes of temporo-parieto-occipital (TPO) and frontal disconnections or resections in children with drug-resistant epilepsy (DRE) in order to determine factors which may predict surgical results. METHODS: Children with DRE, who underwent either TPO or frontal disconnection or resection at Great Ormond Street Hospital for Children between 2000 and 2017, were identified from a prospectively collated operative database. Demographic data, age at surgery, type of surgery, scalp EEGs and operative histopathology were collected. Magnetic resonance imaging (MRI) was assessed to determine completeness of disconnection and presence of radiological lesion beyond the disconnection margins. Seizure outcome at 6, 12, and 24 months post-surgery was assessed using the Engel Scale (ES). Logistic regression was used to identify relationships between data variables and seizure outcome. RESULTS: 46 children (males = 28, females = 18; age range 0.5-16.6 years) who underwent TPO (n = 32, including a re-do disconnection) or frontal disconnection or resection (n = 15) were identified. Patients in the TPO treatment group had more favourable seizure outcomes than those in the frontal treatment group (ES I-II in 56 %vs 47 % at 6 months, 52 % vs 46 % at 12 months). Presence of the lesion beyond disconnection boundaries and older age at the time of surgery were associated with poorer seizure outcome. Gender, surgery type, completeness of disconnection, scalp EEG findings and underlying pathology were not related to seizure outcome, but subgroup numbers were small. CONCLUSIONS: Both TPO and frontal disconnection are effective treatments for selected children with posterior multi-lobar or diffuse frontal lobe epilepsy. Confinement of the MRI lesion within the disconnection margins and a younger age at surgery are associated with favourable seizure outcomes. Further studies are required to elucidate these findings.

Design of an Adhesive Film-Based Microfluidic Device for Alginate Hydrogel-Based Cell Encapsulation.

To support the increasing translational use of transplanted cells, there is a need for high-throughput cell encapsulation technologies. Microfluidics is a particularly promising candidate technology to address this need, but conventional polydimethylsiloxane devices have encountered challenges that have limited their utility, including clogging, leaking, material swelling, high cost, and limited scalability. Here, we use a rapid prototyping approach incorporating patterned adhesive thin films to develop a reusable microfluidic device that can produce alginate hydrogel microbeads with high-throughput potential for microencapsulation applications. We show that beads formed in our device have high sphericity and monodispersity. We use the system to demonstrate effective cell encapsulation of mesenchymal stem cells and show that they can be maintained in culture for at least 28 days with no measurable reduction in viability. Our approach is highly scalable and will support diverse translational applications of microencapsulated cells.

Perceptions of Artificial Intelligence Among Healthcare Staff: A Qualitative Survey Study.

Objectives: The medical community is in agreement that artificial intelligence (AI) will have a radical impact on patient care in the near future. The purpose of this study is to assess the awareness of AI technologies among health professionals and to investigate their perceptions toward AI applications in medicine. Design: A web-based Google Forms survey was distributed via the Royal Free London NHS Foundation Trust e-newsletter. Setting: Only staff working at the NHS Foundation Trust received an invitation to complete the online questionnaire. Participants: 98 healthcare professionals out of 7,538 (response rate 1.3%; CI 95%; margin of error 9.64%) completed the survey, including medical doctors, nurses, therapists, managers, and others. Primary outcome: To investigate the prior knowledge of health professionals on the subject of AI as well as their attitudes and worries about its current and future applications. Results: 64% of respondents reported never coming across applications of AI in their work and 87% did not know the difference between machine learning and deep learning, although 50% knew at least one of the two terms. Furthermore, only 5% stated using speech recognition or transcription applications on a daily basis, while 63% never utilize them. 80% of participants believed there may be serious privacy issues associated with the use of AI and 40% considered AI to be potentially even more dangerous than nuclear weapons. However, 79% also believed AI could be useful or extremely useful in their field of work and only 10% were worried AI will replace them at their job. Conclusions: Despite agreeing on the usefulness of AI in the medical field, most health professionals lack a full understanding of the principles of AI and are worried about potential consequences of its widespread use in clinical practice. The cooperation of healthcare workers is crucial for the integration of AI into clinical practice and without it the NHS may miss out on an exceptionally rewarding opportunity. This highlights the need for better education and clear regulatory frameworks.