Effects and interaction of single nucleotide polymorphisms at the pharmacokinetic/pharmacodynamic site: insights from the Rotterdam study into metformin clinical response and dose titration.

Our study investigated the impact of genetic variations on metformin glycemic response in a cohort from the Rotterdam Study, comprising 14,926 individuals followed for up to 27 years. Among 1285 metformin users of European ancestry, using linear mixed models, we analyzed the association of single nucleotide polymorphisms (SNPs) and a Polygenic Risk Score (PRS) with glycemic response, measured by changes in metformin dosage or HbA1c levels. While individual genetic variants showed no significant association, rs622342 on SLC2A1 correlated with increased glycemic response only in metformin monotherapy patients (ß = -2.09, P-value < 0.001). The collective effect of variants, as represented by PRS, weakly correlated with changes in metformin dosage (ß = 0.023, P-value = 0.027). Synergistic interaction was observed between rs7124355 and rs8192675. Our findings suggest that while higher PRS correlates with increased metformin dosage, its modest effect size limits clinical utility, emphasizing the need for future research in diverse populations to refine genetic risk models.

Glutamate dynamics and BOLD response during OCD symptom provocation in the lateral occipital cortex: A 7 Tesla fMRI-fMRS study.

Obsessive-compulsive disorder (OCD) is linked with dysfunction in frontal-striatal, fronto-limbic, and visual brain regions. Research using proton magnetic resonance spectroscopy (1H-MRS) suggests that altered neurometabolite levels, like glutamate, may contribute to this dysfunction. However, static neurometabolite levels in OCD patients have shown inconsistent results, likely due to previous studies' limited focus on neurometabolite dynamics. We employ functional MRS (fMRS) and functional magnetic resonance imaging (fMRI) to explore these dynamics and brain activation during OCD symptom provocation. We utilized a combined 7-tesla fMRI-fMRS setup to examine task-related BOLD response and glutamate changes in the lateral occipital cortex (LOC) of 30 OCD participants and 34 matched controls during an OCD-specific symptom provocation task. The study examined main effects and between-group differences in brain activation and glutamate levels during the task. A whole sample task-effects analysis on data meeting predefined quality criteria showed significant glutamate increases (n = 41 (22 OCD, 19 controls), mean change: 3.2 %, z = 3.75, p < .001) and task activation (n = 54 (26 OCD, 28 controls), p < .001) in the LOC during OCD blocks compared to neutral blocks. However, no differences in task-induced glutamate dynamics or activation between groups were found, nor a correlation between glutamate levels and task activation. We were able to measure task-induced increases in glutamate and BOLD levels, emphasizing its feasibility for OCD research. The absence of group differences highlights the need for further exploration to discern to what extent neurometabolite dynamics differ between OCD patients and controls. Once established, future studies can use pre-post intervention fMRS-fMRI to probe the effects of therapies modulating glutamate pathways in OCD.

BOATMAP: Bayesian Optimization Active Targeting for Monomorphic Arrhythmia Pace-mapping.

Recent advances in machine learning and deep learning have presented new opportunities for learning to localize the origin of ventricular activation from 12-lead electrocardiograms (ECGs), an important step in guiding ablation therapies for ventricular tachycardia. Passively learning from population data is faced with challenges due to significant variations among subjects, and building a patient-specific model raises the open question of where to select pace-mapping data for training. This work introduces BOATMAP, a novel active learning approach designed to provide clinicians with interpretable guidance that progressively assists in locating the origin of ventricular activation from 12-lead ECGs. BOATMAP inverts the input-output relationship in traditional machine learning solutions to this problem and learns the similarity between a target ECG and a paced ECG as a function of the pacing site coordinates. Using Gaussian processes (GP) as a surrogate model, BOATMAP iteratively refines the estimated similarity landscape while providing suggestions to clinicians regarding the next optimal pacing site. Furthermore, it can incorporate constraints to avoid suggesting pacing in non-viable regions such as the core of the myocardial scar. Tested in a realistic simulation environment in various heart geometries and tissue properties, BOATMAP demonstrated the ability to accurately localize the origin of activation, achieving an average localization accuracy of 3.9±3.6mm with only 8.0±4.0 pacing sites. BOATMAP offers real-time interpretable guidance for accurate localization and enhancing clinical decision-making.

Improved RSV preF protein vaccine quality and stability by elucidation of supercooling-induced aggregation phenomena.

Through a synergistic collaboration of people with varying backgrounds and expertise, the root-cause of respiratory syncytial virus prefusion (preF) protein aggregation during freezing was identified to be supercooling. This issue was addressed through a comprehensive understanding of the product. Leveraging innovative and unconventional methods, apparatus, and approaches, it was effectively determined that key parameters influencing aggregation were the nucleation temperature and the duration of supercooling. Moreover, additional measurements revealed that a transition from the preF to the postfusion conformation occurs upon supercooling, which is likely caused by cold denaturation. The importance of considering freezing conditions is highlighted supporting analytical sampling and envisioning that better understanding of sample handling/freezing process can be applied to a wide range of protein-based products.

The putative role of the habenula in animal migration.

BACKGROUND: When an addicted animal seeks a specific substance, it is based on the perception of internal and external cues that strongly motivate to pursue the acquisition of that compound. In essence, a similar process acts out when an animal leaves its present area to begin its circannual migration. This review article examines the existence of scientific evidence for possible relatedness of migration and addiction by influencing Dorsal Diencephalic Conduction System (DDCS) including the habenula. METHODS: For this review especially the databases of Pubmed and Embase were frequently and non-systematically searched. RESULTS: The mechanisms of bird migration have been thoroughly investigated. Especially the mechanism of the circannual biorhythm and its associated endocrine regulation has been well elucidated. A typical behavior called "Zugunruhe" marks the moment of leaving in migratory birds. The role of magnetoreception in navigation has also been clarified in recent years. However, how bird migration is regulated at the neuronal level in the forebrain is not well understood. Among mammals, marine mammals are most similar to birds. They use terrestrial magnetic field when navigating and often bridge long distances between breeding and foraging areas. Population migration is further often seen among the large hoofed mammals in different parts of the world. Importantly, learning processes and social interactions with conspecifics play a major role in these ungulates. Considering the evolutionary development of the forebrain in vertebrates, it can be postulated that the DDCS plays a central role in regulating the readiness and intensity of essential (emotional) behaviors. There is manifold evidence that this DDCS plays an important role in relapse to abuse after prolonged periods of abstinence from addictive behavior. It is also possible that the DDCS plays a role in navigation. CONCLUSIONS: The role of the DDCS in the neurobiological regulation of bird migration has hardly been investigated. The involvement of this system in relapse to addiction in mammals might suggest to change this. It is recommended that particularly during "Zugunruhe" the role of neuronal regulation via the DDCS will be further investigated.

Inferring clonal somatic mutations directed by X chromosome inactivation status in single cells.

Analysis of clonal dynamics in human tissues is enabled by somatic genetic variation. Here, we show that analysis of mitochondrial mutations in single cells is dramatically improved in females when using X chromosome inactivation to select informative clonal mutations. Applying this strategy to human peripheral mononuclear blood cells reveals clonal structures within T cells that otherwise are blurred by non-informative mutations, including the separation of gamma-delta T cells, suggesting this approach can be used to decipher clonal dynamics of cells in human tissues.

Remote physical activity intervention to promote physical activity and health in adolescent girls (the HERizon project): a multi-arm, pilot randomised trial.

BACKGROUND: Engaging in physical activity (PA) during adolescence is beneficial for health and positive development. However, most adolescent girls have low PA levels, and there is a need for interventions outside of school hours. This pilot randomised controlled trial aimed to explore the preliminary effectiveness of three different remote PA interventions in increasing adolescent girls' moderate-to- vigorous PA (MVPA), fitness and psychosocial outcomes. METHODS: Girls living in the UK or Ireland, aged between 13 and 16 years old, who wished to increase their activity levels, were eligible for the study. Using a random number generator, participants (n = 153; 14.8y ± 1.4) were randomised into one of three 12-week intervention groups (i) PA programme, (ii) Behaviour change support, or (iii) Combined PA programme and Behaviour change support, or (iv) a Comparison group. Outcome measures included accelerometer and self-reported PA, physical fitness (cardiorespiratory fitness; 20 m shuttle run, muscular endurance; push up, muscular strength; long jump), and psychosocial assessments (perceived competence; body appreciation; self-esteem; behavioural regulation). Linear mixed models were used to analyse differences between each intervention arm and the comparison group immediately postintervention (12 weeks) and at follow up (3-months post-intervention), while adjusting for potential confounders. RESULTS: Participation in the PA programme group was associated with higher perceived competence (0.6, 95% CI 0.1 to 1.2), identified regulation (0.7, 95% CI 0.2 to 1.1) and intrinsic motivation (0.9, 95% CI 0.2 to 1.6) at post-intervention. Participation in the Behaviour change group was associated with higher perceived competence at post-intervention (0.6, 95% CI 0.1 to 1.2), and higher push-up scores at the 3-month follow-up (4.0, 95% CI 0.0 to 7.0). Participation in the Combined group was also associated with higher perceived competence at post-intervention (0.8, 95% CI 0.2 to 1.4), and higher push-up scores at the 3-month follow-up (5.0, 95% CI 1.0 to 8.0). No other significant differences were found between the intervention arms and the comparison group. CONCLUSION: Results suggest perceived competence increased across all intervention arms, while the PA programme group enhanced autonomous motivation in the short term. Intervention arms with behaviour change support appear most promising in improving muscular endurance. However, a larger scale trial is needed for a better understanding of between-group differences and the impact of intervention arms on MVPA and fitness, given the small sample size and short-term follow-up.

Prediction of the ground state for indenofluorene-type systems with Clar's π-sextet model.

This study introduces the Ground State Stability (GSS) rule that allows predicting the nature of the ground state of indenofluorene (IF)-type systems from the simple counting of the Clar's π-sextets in the closed- and open-shell configurations. The IF-type system exhibits a triplet ground state when acquiring double or more the number of Clar's π-sextets in the open-shell form relative to the closed-shell form; otherwise, it assumes an open-shell singlet ground state. Performed state-of-the-art DFT calculations and analysis of aromaticity for the systems of interest validate the effectiveness of the proposed rule. We demonstrate that aromaticity plays the most crucial role in determining the ground electronic state for such polycyclic hydrocarbons. The simplicity of the GSS rule makes it a robust strategy for identifying promising systems in the development of indenofluorene-type materials.

Single-cell new RNA sequencing reveals principles of transcription at the resolution of individual bursts.

Analyses of transcriptional bursting from single-cell RNA-sequencing data have revealed patterns of variation and regulation in the kinetic parameters that could be inferred. Here we profiled newly transcribed (4-thiouridine-labelled) RNA across 10,000 individual primary mouse fibroblasts to more broadly infer bursting kinetics and coordination. We demonstrate that inference from new RNA profiles could separate the kinetic parameters that together specify the burst size, and that the synthesis rate (and not the transcriptional off rate) controls the burst size. Importantly, transcriptome-wide inference of transcriptional on and off rates provided conclusive evidence that RNA polymerase II transcribes genes in bursts. Recent reports identified examples of transcriptional co-bursting, yet no global analyses have been performed. The deep new RNA profiles we generated with allelic resolution demonstrated that co-bursting rarely appears more frequently than expected by chance, except for certain gene pairs, notably paralogues located in close genomic proximity. Altogether, new RNA single-cell profiling critically improves the inference of transcriptional bursting and provides strong evidence for independent transcriptional bursting of mammalian genes.

Cognitive behavioural therapy and third-wave approaches for anxiety and related disorders in older people.

BACKGROUND: Cognitive behavioural therapy (CBT) is the most researched psychological therapy for anxiety disorders in adults, and known to be effective in this population. However, it remains unclear whether these results apply to older adults, as most studies include participants between 18 and 55 years of age. This systematic review aims to provide a comprehensive and up-to-date synthesis of the available evidence on CBT and third wave approaches for older adults with anxiety and related disorders. OBJECTIVES: To assess the effects of Cognitive Behavioural Therapy (CT, BT, CBT and third-wave CBT interventions) on severity of anxiety symptoms compared with minimal management (not providing therapy) for anxiety and related disorders in older adults, aged 55 years or over. To assess the effects of CBT and related therapies on severity of anxiety symptoms compared with other psychological therapies for anxiety and related disorders in older adults, aged 55 years or over. SEARCH METHODS: We searched the Cochrane Common Mental Disorders Controlled studies Register (CCMDCTR), CENTRAL, Ovid MEDLINE, Ovid Embase and Ovid PsycINFO to 21 July 2022. These searches were updated on 2 February 2024. We also searched the international studies registries, including Clinicalstudies.gov and the WHO International Clinical Trials Registry Platform (ICTRP), to identify additional ongoing and unpublished studies. These sources were manually searched for studies up to 12 February 2024. SELECTION CRITERIA: We included randomised controlled trials (RCTs) in older adults (≥ 55 years) with an anxiety disorder, or a related disorder, including obsessive compulsive disorder (OCD), acute stress disorder and post-traumatic stress disorder (PTSD), that compared CBT to either minimal management or an active (non-CBT) psychological therapy. Eligible studies had to have an anxiety-related outcome. DATA COLLECTION AND ANALYSIS: Several authors independently screened all titles identified by the searches. All full texts were screened for eligibility according to our prespecified selection criteria. Data were extracted and the risk of bias was assessed using the Cochrane tool for RCTs. The certainty of evidence was evaluated using GRADE. Meta-analyses were performed for outcomes with quantitative data from more than one study. MAIN RESULTS: We included 21 RCTs on 1234 older people allocated to either CBT or control conditions. Ten studies focused on generalised anxiety disorder; others mostly included a mix of clinical diagnoses. Nineteen studies focused on the comparison between CBT and minimal management. Key issues relating to risk of bias were lack of blinding of participants and personnel, and participants dropping out of studies, potentially due to treatment preference and allocation. CBT may result in a small-to-moderate reduction of anxiety post-treatment (SMD -0.51, 95% CI -0.66 to -0.36, low-certainty evidence). However, compared to this benefit with CBT immediately after treatment, at three to six months post-treatment, there was little to no difference between CBT and minimal management (SMD -0.29, 95% CI -0.59 to 0.01, low-certainty evidence). CBT may have little or no effect on clinical recovery/ improvement post-treatment compared to minimal management, but the evidence is very uncertain (RR 1.56, 95% CI 1.20 to 2.03, very low-certainty evidence). Results indicate that five people would need to receive treatment for one additional person to benefit (NNTB = 5). Compared to minimal management, CBT may result in a reduction of comorbid depression symptoms post-treatment (SMD -0.57, 95% CI -0.74 to -0.40, low-certainty evidence). There was no difference in dropout rates post-treatment, although the certainty of the evidence was low (RR 1.19, 95% CI 0.80 to 1.78). Two studies reported adverse events, both of which related to medication in the control groups (very low-certainty evidence, no quantitative estimate). Only two studies compared CBT to other psychological therapies, both of which only included participants with post-traumatic stress disorder. Low-certainty evidence showed no difference in anxiety severity post-treatment and at four to six months post-treatment, symptoms of depression post-treatment, and dropout rates post-treatment. Other outcomes and time points are reported in the results section of the manuscript. AUTHORS' CONCLUSIONS: CBT may be more effective than minimal management in reducing anxiety and symptoms of worry and depression post-treatment in older adults with anxiety disorders. The evidence is less certain longer-term and for other outcomes including clinical recovery/improvement. There is not enough evidence to determine whether CBT is more effective than alternative psychological therapies for anxiety in older adults.

Multifaceted, Brief Intensive Home-Based Exposure Treatment in Patients with Obsessive-Compulsive Disorder Who are Nonresponsive to Regular Cognitive Behavior Therapy: An Uncontrolled Pilot Study.

OBJECTIVE: To test a multifaceted treatment program for patients with obsessive-compulsive disorder (OCD) who did not respond to regular cognitive behavior therapy (CBT). The treatment addresses several factors that may play a role in maintaining OCD. METHODS: We designed a treatment consisting of a 6-day intensive, individual exposure in vivo with response prevention (ERP) format, with 24 therapist-assisted treatment hours at the patient's home and 12 self-controlled ERP hours, including behavioral activation and family interventions. Next, we investigated the effect (obsessive-compulsive symptoms, comorbidity, functioning, quality of life, OCD-related interaction patterns) and feasibility (dropout, treatment satisfaction, and organization) of this program using pre-post-tests, pre-follow-up tests, and qualitative data from patients, family members, and therapists. RESULTS: In a sample of 22 participants, obsessive-compulsive symptoms (Y-BOCS pre: 28.7, post: 15.9; Wilcoxon S-R tests P<0.01) improved significantly, as did most other effect measures. Results were largely, but not completely, preserved at 3-month follow-up. There was only 1 dropout. Patients, family members, and therapists were satisfied with the treatment. Implementation of the treatment did not pose difficulties. CONCLUSIONS: In nonresponders with OCD, a multifaceted, brief, intensive home-based ERP program targeting factors maintaining OCD is promising and feasible. Extra care is needed to maintain improvement.

pyCEPS: A cross-platform electroanatomic mapping data to computational model conversion platform for the calibration of digital twin models of cardiac electrophysiology.

BACKGROUND AND OBJECTIVE: Data from electro-anatomical mapping (EAM) systems are playing an increasingly important role in computational modeling studies for the patient-specific calibration of digital twin models. However, data exported from commercial EAM systems are challenging to access and parse. Converting to data formats that are easily amenable to be viewed and analyzed with commonly used cardiac simulation software tools such as openCARP remains challenging. We therefore developed an open-source platform, pyCEPS, for parsing and converting clinical EAM data conveniently to standard formats widely adopted within the cardiac modeling community. METHODS AND RESULTS: pyCEPS is an open-source Python-based platform providing the following functions: (i) access and interrogate the EAM data exported from clinical mapping systems; (ii) efficient browsing of EAM data to preview mapping procedures, electrograms (EGMs), and electro-cardiograms (ECGs); (iii) conversion to modeling formats according to the openCARP standard, to be amenable to analysis with standard tools and advanced workflows as used for in silico EAM data. Documentation and training material to facilitate access to this complementary research tool for new users is provided. We describe the technological underpinnings and demonstrate the capabilities of pyCEPS first, and showcase its use in an exemplary modeling application where we use clinical imaging data to build a patient-specific anatomical model. CONCLUSION: With pyCEPS we offer an open-source framework for accessing EAM data, and converting these to cardiac modeling standard formats. pyCEPS provides the core functionality needed to integrate EAM data in cardiac modeling research. We detail how pyCEPS could be integrated into model calibration workflows facilitating the calibration of a computational model based on EAM data.

Patient Perceptions of Illness Causes and Treatment Preferences for Obsessive-Compulsive Disorder: A Mixed-Methods Study.

Obsessive-compulsive disorder (OCD) is a condition with high patient morbidity and mortality. Research shows that eliciting patient explanations about illness causes and treatment preferences promotes cross-cultural work and engagement in health services. These topics are in the Cultural Formulation Interview (CFI), a semi-structured interview first published in DSM-5 that applies anthropological approaches within mental health services to promote person-centered care. This study focuses on the New York City site of an international multi-site study that used qualitative-quantitative mixed methods to: (1) analyze CFI transcripts with 55 adults with OCD to explore perceived illness causes and treatment preferences, and (2) explore whether past treatment experiences are related to perceptions about causes of current symptoms. The most commonly named causes were circumstantial stressors (n = 16), genetics (n = 12), personal psychological traits (n = 9), an interaction between circumstantial stressors and participants' brains (n = 6), and a non-specific brain problem (n = 6). The most common treatment preferences were psychotherapy (n = 42), anything (n = 4), nothing (n = 4), and medications (n = 2). Those with a prior medication history had twice the odds of reporting a biological cause, though this was not a statistically significant difference. Our findings suggest that providers should ask patients about illness causes and treatment preferences to guide treatment choice.

Does a lack of emotions make chatbots unfit to be psychotherapists?

Mental health chatbots (MHCBs) designed to support individuals in coping with mental health issues are rapidly advancing. Currently, these MHCBs are predominantly used in commercial rather than clinical contexts, but this might change soon. The question is whether this use is ethically desirable. This paper addresses a critical yet understudied concern: assuming that MHCBs cannot have genuine emotions, how this assumption may affect psychotherapy, and consequently the quality of treatment outcomes. We argue that if MHCBs lack emotions, they cannot have genuine (affective) empathy or utilise countertransference. Consequently, this gives reason to worry that MHCBs are (a) more liable to harm and (b) less likely to benefit patients than human therapists. We discuss some responses to this worry and conclude that further empirical research is necessary to determine whether these worries are valid. We conclude that, even if these worries are valid, it does not mean that we should never use MHCBs. By discussing the broader ethical debate on the clinical use of chatbots, we point towards how further research can help us establish ethical boundaries for how we should use mental health chatbots.

Reducing suicides in mental healthcare: results from a 4-year follow-up implementation study in the Netherlands (SUPRANET).

Objective: In 2016, the SUicide PRevention Action NETwork (SUPRANET) was launched. The SUPRANET intervention aims at better implementing the suicide prevention guideline. An implementation study was developed to evaluate the impact of SUPRANET over time on three outcomes: 1) suicides, 2) registration of suicide attempts, and 3) professionals' knowledge and adherence to the guideline. Methods: This study included 13 institutions, and used an uncontrolled longitudinal prospective design, collecting biannual data on a 2-level structure (institutional and team level). Suicides and suicide attempts were extracted from data systems. Professionals' knowledge and adherence were measured using a self-report questionnaire. A three-step interrupted time series analysis (ITSA) was performed for the first two outcomes. Step 1 assessed whether institutions executed the SUPRANET intervention as intended. Step 2 examined if institutions complied with the four guideline recommendations. Based on steps 1 and 2, institutions were classified as below or above average and after that, included as moderators in step 3 to examine the effect of SUPRANET over time compared to the baseline. The third outcome was analyzed with a longitudinal multilevel regression analysis, and tested for moderation. Results: After institutions were labeled based on their efforts and investments made (below average vs above average), we found no statistically significant difference in suicides (standardized mortality ratio) between the two groups relative to the baseline. Institutions labeled as above average did register significantly more suicide attempts directly after the start of the intervention (78.8 per 100,000 patients, p<0.001, 95%CI=(51.3 per 100,000, 106.4 per 100,000)), and as the study progressed, they continued to report a significantly greater improvement in the number of registered attempts compared with institutions assigned as below average (8.7 per 100,000 patients per half year, p=0.004, 95%CI=(3.3 per 100,000, 14.1 per 100,000)). Professionals working at institutions that invested more in the SUPRANET activities adhered significantly better to the guideline over time (b=1.39, 95%CI=(0.12,2.65), p=0.032). Conclusion: Institutions labeled as above average registered significantly more suicide attempts and also better adhered to the guideline compared with institutions that had performed less well. Although no convincing intervention effect on suicides was found within the study period, we do think that this network is potentially able to reduce suicides. Continuous investments and fully implementing as many guideline recommendations as possible are essential to achieve the biggest drop in suicides.

ForCEPSS-A framework for cardiac electrophysiology simulations standardization.

BACKGROUND AND OBJECTIVE: Simulation of cardiac electrophysiology (CEP) is an important research tool that is increasingly being adopted in industrial and clinical applications. Typical workflows for CEP simulation consist of a sequence of processing stages starting with building an anatomical model and then calibrating its electrophysiological properties to match observable data. While the calibration stages are common and generalizable, most CEP studies re-implement these steps in complex and highly variable workflows. This lack of standardization renders the execution of computational CEP studies in an efficient, robust, and reproducible manner a significant challenge. Here, we propose ForCEPSS as an efficient and robust, yet flexible, software framework for standardizing CEP simulation studies. METHODS AND RESULTS: Key processing stages of CEP simulation studies are identified and implemented in a standardized workflow that builds on openCARP1 Plank et al. (2021) and the Python-based carputils2 framework. Stages include (i) the definition and initialization of action potential phenotypes, (ii) the tissue scale calibration of conduction properties, (iii) the functional initialization to approximate a limit cycle corresponding to the dynamic reference state according to an experimental protocol, and, (iv) the execution of the CEP study where the electrophysiological response to a perturbation of the limit cycle is probed. As an exemplar application, we employ ForCEPSS to prepare a CEP study according to the Virtual Arrhythmia Risk Prediction protocol used for investigating the arrhythmogenic risk of developing infarct-related ventricular tachycardia (VT) in ischemic cardiomyopathy patients. We demonstrate that ForCEPSS enables a fully automated execution of all stages of this complex protocol. CONCLUSION: ForCEPSS offers a novel comprehensive, standardized, and automated CEP simulation workflow. The high degree of automation accelerates the execution of CEP simulation studies, reduces errors, improves robustness, and makes CEP studies reproducible. Verification of simulation studies within the CEP modeling community is thus possible. As such, ForCEPSS makes an important contribution towards increasing transparency, standardization, and reproducibility of in silico CEP experiments.

Microsampling Techniques Suitable for Therapeutic Drug Monitoring of Antipsychotics.

BACKGROUND: Therapeutic drug monitoring (TDM) of antipsychotics for dose titration or detection of noncompliance is not uncommon in daily practice. Normally, TDM implies measuring a drug concentration in venous blood samples. This technique is invasive and requires trained assistants and patients normally need to go to an outpatient clinic. Over the past decades, sensitivity of analytical equipment has improved leading to a growing interest in microsampling techniques. These techniques are minimally invasive, require a small volume (<100 µL), usually result in stable samples, and can be collected by the patient or a caregiver at home. Before a microsampling technique can be used in daily routine, proper method development and a clinical validation study should be performed. METHOD: For this review, the databases of PubMed and Embase were systematically searched. Currently available microsampling techniques for antipsychotics in blood, serum, or plasma are summarized. Subsequently, it has also been assessed whether these techniques are sufficiently validated for TDM monitoring in daily practice. RESULTS: Several microsampling techniques are available today, for example, dried blood spot sampling, dried plasma extraction cards, and volumetric absorptive microsampling. Eighteen studies were identified in which a microsampling technique for 1 or a few antipsychotics was chemically analytically and clinically validated. However, the majority of these studies have relevant shortcomings that mean its usefulness for different antipsychotics is not yet well established. CONCLUSIONS: Microsampling for TDM can be recommended for patients using clozapine. For TDM of other antipsychotics, it is a very promising development.

Predicting postinfarct ventricular tachycardia by integrating cardiac MRI and advanced computational reentrant pathway analysis.

BACKGROUND: Implantable cardiac defibrillator (ICD) implantation can protect against sudden cardiac death after myocardial infarction. However, improved risk stratification for device requirement is still needed. OBJECTIVE: The purpose of this study was to improve assessment of postinfarct ventricular electropathology and prediction of appropriate ICD therapy by combining late gadolinium enhancement (LGE) and advanced computational modeling. METHODS: ADAS 3D LV (ADAS LV Medical, Barcelona, Spain) and custom-made software were used to generate 3-dimensional patient-specific ventricular models in a prospective cohort of patients with a myocardial infarction (N = 40) having undergone LGE imaging before ICD implantation. Corridor metrics and 3-dimensional surface features were computed from LGE images. The Virtual Induction and Treatment of Arrhythmias (VITA) framework was applied to patient-specific models to comprehensively probe the vulnerability of the scar substrate to sustaining reentrant circuits. Imaging and VITA metrics, related to the numbers of induced ventricular tachycardias and their corresponding round trip times (RTTs), were compared with ICD therapy during follow-up. RESULTS: Patients with an event (n = 17) had a larger interface between healthy myocardium and scar and higher VITA metrics. Cox regression analysis demonstrated a significant independent association with an event: interface (hazard ratio [HR] 2.79; 95% confidence interval [CI] 1.44-5.44; P < .01), unique ventricular tachycardias (HR 1.67; 95% CI 1.04-2.68; P = .03), mean RTT (HR 2.14; 95% CI 1.11-4.12; P = .02), and maximum RTT (HR 2.13; 95% CI 1.19-3.81; P = .01). CONCLUSION: A detailed quantitative analysis of LGE-based scar maps, combined with advanced computational modeling, can accurately predict ICD therapy and could facilitate the early identification of high-risk patients in addition to left ventricular ejection fraction.

Hybrid Neural State-Space Modeling for Supervised and Unsupervised Electrocardiographic Imaging.

State-space modeling (SSM) provides a general framework for many image reconstruction tasks. Error in a priori physiological knowledge of the imaging physics, can bring incorrectness to solutions. Modern deep-learning approaches show great promise but lack interpretability and rely on large amounts of labeled data. In this paper, we present a novel hybrid SSM framework for electrocardiographic imaging (ECGI) to leverage the advantage of state-space formulations in data-driven learning. We first leverage the physics-based forward operator to supervise the learning. We then introduce neural modeling of the transition function and the associated Bayesian filtering strategy. We applied the hybrid SSM framework to reconstruct electrical activity on the heart surface from body-surface potentials. In unsupervised settings of both in-silico and in-vivo data without cardiac electrical activity as the ground truth to supervise the learning, we demonstrated improved ECGI performances of the hybrid SSM framework trained from a small number of ECG observations in comparison to the fixed SSM. We further demonstrated that, when in-silico simulation data becomes available, mixed supervised and unsupervised training of the hybrid SSM achieved a further 40.6% and 45.6% improvements, respectively, in comparison to traditional ECGI baselines and supervised data-driven ECGI baselines for localizing the origin of ventricular activations in real data.