Multifunctional host-defense peptides isolated from skin secretions of the banana tree dwelling frog Boana platanera (Hylidae; Hylinae).

Five host-defense peptides (figainin 2PL, hylin PL, raniseptin PL, plasticin PL, and peptide YL) were isolated from norepinephrine-stimulated skin secretions of the banana tree dwelling frog Boana platanera (Hylidae; Hylinae) collected in Trinidad. Raniseptin PL (GVFDTVKKIGKAVGKFALGVAKNYLNS.NH2) and figainin 2PL (FLGTVLKLGKAIAKTVVPMLTNAMQPKQ. NH2) showed potent and rapid bactericidal activity against a range of clinically relevant Gram-positive and Gram-negative ESKAPE + pathogens and Clostridioides difficile. The peptides also showed potent cytotoxic activity (LC50 values < 30 µM) against A549, MDA-MB-231 and HT29 human tumor-derived cell lines but appreciably lower hemolytic activity against mouse erythrocytes (LC50 = 262 ± 14 µM for raniseptin PL and 157 ± 16 µM for figainin 2PL). Hylin PL (FLGLIPALAGAIGNLIK.NH2) showed relatively weak activity against microorganisms but was more hemolytic. The glycine-leucine-rich peptide with structural similarity to the plasticins (GLLSTVGGLVGGLLNNLGL.NH2) and the non-cytotoxic peptide YL (YVPGVIESLL.NH2) lacked antimicrobial and cytotoxic activities. Hylin PL, raniseptinPL and peptide YL stimulated the rate of release of insulin from BRIN-BD11 clonal ß-cells at concentrations ≥100 nM. Peptide YL was the most effective (2.3-fold increase compared with basal rate at 1 µM concentration) and may represent a template for the design of a new class of incretin-based anti-diabetic drugs.

AI in patient flow: applications of artificial intelligence to improve patient flow in NHS acute mental health inpatient units.

INTRODUCTION: Growing demand for mental health services, coupled with funding and resource limitations, creates an opportunity for novel technological solutions including artificial intelligence (AI). This study aims to identify issues in patient flow on mental health units and align them with potential AI solutions, ultimately devising a model for their integration at service level. METHOD: Following a narrative literature review and pilot interview, 20 semi-structured interviews were conducted with AI and mental health experts. Thematic analysis was then used to analyse and synthesise gathered data and construct an enhanced model. RESULTS: Predictive variables for length-of-stay and readmission rate are not consistent in the literature. There are, however, common themes in patient flow issues. An analysis identified several potential areas for AI-enhanced patient flow. Firstly, AI could improve patient flow by streamlining administrative tasks and optimising allocation of resources. Secondly, real-time data analytics systems could support clinician decision-making in triage, discharge, diagnosis and treatment stages. Finally, longer-term, development of solutions such as digital phenotyping could help transform mental health care to a more preventative, personalised model. CONCLUSIONS: Recommendations were formulated for NHS trusts open to adopting AI patient flow enhancements. Although AI offers many promising use-cases, greater collaborative investment and infrastructure are needed to deliver clinically validated improvements. Concerns around data-use, regulation and transparency remain, and hospitals must continue to balance guidelines with stakeholder priorities. Further research is needed to connect existing case studies and develop a framework for their evaluation.

Applications of artificial intelligence to improve patient flow on mental health inpatient units - Narrative literature review.

BACKGROUND: Despite a growing body of research into both Artificial intelligence and mental health inpatient flow issues, few studies adequately combine the two. This review summarises findings in the fields of AI in psychiatry and patient flow from the past 5 years, finds links and identifies gaps for future research. METHODS: The OVID database was used to access Embase and Medline. Top journals such as JAMA, Nature and The Lancet were screened for other relevant studies. Selection bias was limited by strict inclusion and exclusion criteria. RESEARCH: 3,675 papers were identified in March 2020, of which a limited number focused on AI for mental health unit patient flow. After initial screening, 323 were selected and 83 were subsequently analysed. The literature review revealed a wide range of applications with three main themes: diagnosis (33%), prognosis (39%) and treatment (28%). The main themes that emerged from AI in patient flow studies were: readmissions (41%), resource allocation (44%) and limitations (91%). The review extrapolates those solutions and suggests how they could potentially improve patient flow on mental health units, along with challenges and limitations they could face. CONCLUSION: Research widely addresses potential uses of AI in mental health, with some focused on its applicability in psychiatric inpatients units, however research rarely discusses improvements in patient flow. Studies investigated various uses of AI to improve patient flow across specialities. This review highlights a gap in research and the unique research opportunity it presents.

Ethical Issues in Democratizing Digital Phenotypes and Machine Learning in the Next Generation of Digital Health Technologies.

Digital phenotyping is the term given to the capturing and use of user log data from health and wellbeing technologies used in apps and cloud-based services. This paper explores ethical issues in making use of digital phenotype data in the arena of digital health interventions. Products and services based on digital wellbeing technologies typically include mobile device apps as well as browser-based apps to a lesser extent, and can include telephony-based services, text-based chatbots, and voice-activated chatbots. Many of these digital products and services are simultaneously available across many channels in order to maximize availability for users. Digital wellbeing technologies offer useful methods for real-time data capture of the interactions of users with the products and services. It is possible to design what data are recorded, how and where it may be stored, and, crucially, how it can be analyzed to reveal individual or collective usage patterns. The paper also examines digital phenotyping workflows, before enumerating the ethical concerns pertaining to different types of digital phenotype data, highlighting ethical considerations for collection, storage, and use of the data. A case study of a digital health app is used to illustrate the ethical issues. The case study explores the issues from a perspective of data prospecting and subsequent machine learning. The ethical use of machine learning and artificial intelligence on digital phenotype data and the broader issues in democratizing machine learning and artificial intelligence for digital phenotype data are then explored in detail.

Burn disaster response planning: an urban region's approach.

The objective of this study was to describe a draft response plan for the tiered triage, treatment, or transportation of 400 adult and pediatric victims (50/million population) of a burn disaster for the first 3 to 5 days after injury using regional resources. Review of meeting minutes and the 11 deliverables of the draft response plan was performed. The draft burn disaster response plan developed for NYC recommended: 1) City hospitals or regional burn centers within a 60-mile distance be designated as tiered Burn Disaster Receiving Hospitals (BDRH); 2) these hospitals be divided into a four-tier system, based on clinical resources; and 3) burn care supplies be provided to Tier 3 nonburn centers. Existing burn center referral guidelines were modified into a hierarchical BDRH matrix, which would vector certain patients to local or regional burn centers for initial care until capacity is reached; the remainder would be cared for in nonburn center facilities for up to 3 to 5 days until a city, regional, or national burn bed becomes available. Interfacility triage would be coordinated by a central team. Although recommendations for patient transportation, educational initiatives for prehospital and hospital providers, city-wide, interfacility or interagency communication strategies and coordination at the State or Federal levels were outlined, future initiatives will expound on these issues. An incident resulting in critically injured burn victims exceeding the capacity of local and regional burn center beds may be a reality within any community and warrants a planned response. To address this possibility within New York City, an initial draft of a burn disaster response has been created. A scaleable plan using local, state, regional, or federal health care and governmental institutions was developed.