LSDDEP2: study protocol for a randomised, double-dummy, triple-blind, active placebo-controlled, parallel groups trial of LSD microdosing in patients with major depressive disorder.

Background: Major depressive disorder (MDD) poses a significant global health burden with available treatments limited by inconsistent efficacy and notable side effects. Classic psychedelics, including lysergic acid diethylamide (LSD), have garnered attention for their potential in treating psychiatric disorders. Microdosing, the repeated consumption of sub-hallucinogenic doses of psychedelics, has emerged as a self-treatment approach for depression within lay communities.

DIGIPREDICT: physiological, behavioural and environmental predictors of asthma attacks-a prospective observational study using digital markers and artificial intelligence-study protocol.

Introduction: Asthma attacks are a leading cause of morbidity and mortality but are preventable in most if detected and treated promptly. However, the changes that occur physiologically and behaviourally in the days and weeks preceding an attack are not always recognised, highlighting a potential role for technology. The aim of this study 'DIGIPREDICT' is to identify early digital markers of asthma attacks using sensors embedded in smart devices including watches and inhalers, and leverage health and environmental datasets and artificial intelligence, to develop a risk prediction model to provide an early, personalised warning of asthma attacks.

LSD increases sleep duration the night after microdosing.

Microdosing psychedelic drugs at a level below the threshold to induce hallucinations is an increasingly common lifestyle practice. However, the effects of microdosing on sleep have not been previously reported. Here, we report results from a Phase 1 randomized controlled trial in which 80 healthy adult male volunteers received a 6-week course of either LSD (10 µg) or placebo with doses self-administered every third day.

Towards Personalised Mood Prediction and Explanation for Depression from Biophysical Data.

Digital health applications using Artificial Intelligence (AI) are a promising opportunity to address the widening gap between available resources and mental health needs globally. Increasingly, passively acquired data from wearables are augmented with carefully selected active data from depressed individuals to develop Machine Learning (ML) models of depression based on mood scores. However, most ML models are black box in nature, and hence the outputs are not explainable.

An open-label pilot trial assessing tolerability and feasibility of LSD microdosing in patients with major depressive disorder (LSDDEP1).

Background: Globally, an estimated 260 million people suffer from depression [1], and there is a clear need for the development of new, alternative antidepressant therapies. In light of problems with the tolerability and efficacy of available treatments [2], a global trend is emerging for patients to self-treat depression with microdoses of psychedelic drugs such as lysergic acid diethylamide (LSD) and psilocybin [3]. Beyond anecdotal reports from those who self-medicate in this way, few clinical trials have evaluated this practice.

A framework for the design of a closed-loop gastric pacemaker for treating conduction block.

Background And Objective: Gastrointestinal (GI) motility disorders can be significantly detrimental to the quality of life. Pacing, or long pulse gastric electrical stimulation, is a potential treatment option for treating GI motility disorders by modulating the slow wave activity. Open-loop pacing of the GI tract is the current standard for modulating dysrhythmic patterns, but it is known to be suboptimal and inefficient.

Development of closed-loop modelling framework for adaptive respiratory pacemakers.

Objective: Ventilatory pacing by electrical stimulation of the phrenic nerve has many advantages compared to mechanical ventilation. However, commercially available respiratory pacing devices operate in an open-loop fashion, which require manual adjustment of stimulation parameters for a given patient. Here, we report the model development of a closed-loop respiratory pacemaker, which can automatically adapt to various pathological ventilation conditions and metabolic demands.

Using Acoustic Speech Patterns From Smartphones to Investigate Mood Disorders: Scoping Review.

Background: Mood disorders are commonly underrecognized and undertreated, as diagnosis is reliant on self-reporting and clinical assessments that are often not timely. Speech characteristics of those with mood disorders differs from healthy individuals. With the wide use of smartphones, and the emergence of machine learning approaches, smartphones can be used to monitor speech patterns to help the diagnosis and monitoring of mood disorders.

Compositional cyber-physical epidemiology of COVID-19.

The COVID-19 pandemic has posed significant challenges globally. Countries have adopted different strategies with varying degrees of success. Epidemiologists are studying the impact of government actions using scenario analysis.

A novel approach for model-based design of gastric pacemakers.

Understanding the slow wave propagation patterns of Interstitial Cells of Cajal (ICC) is essential when designing Gastric Electrical Stimulators (GESs) to treat motility disorders. A GES with the ability to both sense and pace, working in closed-loop with the ICC, will enable efficient modulation of Gastrointestinal (GI) dysrhythmias. However, existing GESs targeted at modulating GI dysrhythmias operate in an open-loop and hence their clinical efficacy is uncertain.

Closing the Loop: Validation of Implantable Cardiac Devices With Computational Heart Models.

Objective: Cardiovascular Implantable Electronic Devices (CIEDs) are used extensively for treating life-threatening conditions such as bradycardia, atrioventricular block and heart failure. The complicated heterogeneous physical dynamics of patients provide distinct challenges to device development and validation. We address this problem by proposing a device testing framework within the in-silico closed-loop context of patient physiology.

Resonant model-A new paradigm for modeling an action potential of biological cells.

Organ level simulation of bioelectric behavior in the body benefits from flexible and efficient models of cellular membrane potential. These computational organ and cell models can be used to study the impact of pharmaceutical drugs, test hypotheses, assess risk and for closed-loop validation of medical devices. To move closer to the real-time requirements of this modeling a new flexible Fourier based general membrane potential model, called as a Resonant model, is developed that is computationally inexpensive.

Cardiac Electrical Modeling for Closed-Loop Validation of Implantable Devices.

Objective: Evaluating and testing cardiac electrical devices in a closed-physiologic-loop can help design safety, but this is rarely practical or comprehensive. Furthermore, in silico closed-loop testing with biophysical computer models cannot meet the requirements of time-critical cardiac device systems, while simplified models meeting time-critical requirements may not have the necessary dynamic features. We propose a new high-level (abstracted) physiologically-based computational heart model that is time-critical and dynamic.

A Formal Approach for Scalable Simulation of Gastric ICC Electrophysiology.

Objective: Efficient and accurate organ models are crucial for closed-loop validation of implantable medical devices. This paper investigates bio-electric slow wave modeling of the stomach, so that gastric electrical stimulator (GES) can be validated and verified prior to implantation. In particular, we consider high-fidelity, scalable, and efficient modeling of the pacemaker, Interstitial cells of Cajal (ICC), based on the formal hybrid input output automata (HIOA) framework.

An intracardiac electrogram model to bridge virtual hearts and implantable cardiac devices.

Virtual heart models have been proposed to enhance the safety of implantable cardiac devices through closed loop validation. To communicate with a virtual heart, devices have been driven by cardiac signals at specific sites. As a result, only the action potentials of these sites are sensed.

A Parametric Computational Model of the Action Potential of Pacemaker Cells.

Objective: A flexible, efficient, and verifiable pacemaker cell model is essential to the design of real-time virtual hearts that can be used for closed-loop validation of cardiac devices. A new parametric model of pacemaker action potential is developed to address this need.

Methods: The action potential phases are modeled using hybrid automaton with one piecewise-linear continuous variable.