Neuroscience meets behavior: A systematic literature review on magnetic resonance imaging of the brain combined with real-world digital phenotyping.

A primary goal of neuroscience is to understand the relationship between the brain and behavior. While magnetic resonance imaging (MRI) examines brain structure and function under controlled conditions, digital phenotyping via portable automatic devices (PAD) quantifies behavior in real-world settings. Combining these two technologies may bridge the gap between brain imaging, physiology, and real-time behavior, enhancing the generalizability of laboratory and clinical findings. However, the use of MRI and data from PADs outside the MRI scanner remains underexplored. Herein, we present a Preferred Reporting Items for Systematic Reviews and Meta-Analysis systematic literature review that identifies and analyzes the current state of research on the integration of brain MRI and PADs. PubMed and Scopus were automatically searched using keywords covering various MRI techniques and PADs. Abstracts were screened to only include articles that collected MRI brain data and PAD data outside the laboratory environment. Full-text screening was then conducted to ensure included articles combined quantitative data from MRI with data from PADs, yielding 94 selected papers for a total of N = 14,778 subjects. Results were reported as cross-frequency tables between brain imaging and behavior sampling methods and patterns were identified through network analysis. Furthermore, brain maps reported in the studies were synthesized according to the measurement modalities that were used. Results demonstrate the feasibility of integrating MRI and PADs across various study designs, patient and control populations, and age groups. The majority of published literature combines functional, T1-weighted, and diffusion weighted MRI with physical activity sensors, ecological momentary assessment via PADs, and sleep. The literature further highlights specific brain regions frequently correlated with distinct MRI-PAD combinations. These combinations enable in-depth studies on how physiology, brain function and behavior influence each other. Our review highlights the potential for constructing brain-behavior models that extend beyond the scanner and into real-world contexts.

Current challenges in cell and gene therapy: a joint view from the European Committee of the International Society for Cell & Gene Therapy (ISCT) and the European Society for Blood and Marrow Transplantation (EBMT).

Cell and gene therapy poses evolving challenges. The current article summarizes the discussions held by European Regional Committee of the International Society for Cell & Gene Therapy and the European Society for Blood and Marrow Transplantation (EBMT) on the current challenges in this field, focusing on the European setting. This article emphasizes the imperative assessment of real-world cell and gene therapy activity, advocating for expanded registries beyond hematopoietic transplantation and chimeric antigen receptor-T-cell therapy. Accreditation's role in ensuring standardized procedures, as exemplified by JACIE (The Joint Accreditation Committee of ISCT-Europe and EBMT), is crucial for safety. Access to commercial products and reimbursement variations among countries underscore the need for uniform access to advanced therapy medical products (ATMPs). Academic product development and point-of-care manufacturing face barriers to patient access. Hospital Exemption's potential, demonstrated by some initial experiences, may increase patient accessibility in individual situations. Regulatory challenges, including the ongoing European ATMPs legislation review, necessitate standardized criteria for Hospital Exemption and mandatory reporting within registries. Efforts to combat unproven therapies and fraud involve collaboration between scientific societies, regulatory bodies and patient groups. Finally, is important to highlight the vital role of education and workforce development in meeting the escalating demand for specialized professionals in the ATMP field. Collaboration among scientific societies, academic institutions, industry, regulatory bodies and patient groups is crucial for overcoming all these challenges to increase gene and cell therapy activity in Europe.

Depression is associated with blunted affective responses to naturalistic reward prediction errors.

BACKGROUND: Depression is characterized by abnormalities in emotional processing, but the specific drivers of such emotional abnormalities are unknown. Computational work indicates that both surprising outcomes (prediction errors; PEs) and outcomes (values) themselves drive emotional responses, but neither has been consistently linked to affective disturbances in depression. As a result, the computational mechanisms driving emotional abnormalities in depression remain unknown. METHODS: Here, in 687 individuals, one-third of whom qualify as depressed via a standard self-report measure (the PHQ-9), we use high-stakes, naturalistic events - the reveal of midterm exam grades - to test whether individuals with heightened depression display a specific reduction in emotional response to positive PEs. RESULTS: Using Bayesian mixed effects models, we find that individuals with heightened depression do not affectively benefit from surprising, good outcomes - that is, they display reduced affective responses to positive PEs. These results were highly specific: effects were not observed to negative PEs, value signals (grades), and were not related to generalized anxiety. This suggests that the computational drivers of abnormalities in emotion in depression may be specifically due to positive PE-based emotional responding. CONCLUSIONS: Affective abnormalities are core depression symptoms, but the computational mechanisms underlying such differences are unknown. This work suggests that blunted affective reactions to positive PEs are likely mechanistic drivers of emotional dysregulation in depression.

Neurophysiological responses to emotional faces predict dynamic fluctuations in affect in adolescents.

The ability to accurately identify and interpret others' emotions is critical for social and emotional functioning during adolescence. Indeed, previous research has identified that laboratory-based indices of facial emotion recognition and engagement with emotional faces predict adolescent mood states. Whether socioemotional information processing relates to real-world affective dynamics using an ecologically sensitive approach, however, has rarely been assessed. In the present study, adolescents (N = 62; ages 13-18) completed a Facial Recognition Task, including happy, angry, and sad stimuli, while EEG data were acquired. Participants also provided ecological momentary assessment (EMA) data probing their current level of happiness, anger, and sadness for 1-week, resulting in indices of emotion (mean-level, inertia, instability). Analyses focused on relations between (1) accuracy for and (2) prolonged engagement with (LPP) emotional faces and EMA-reported emotions. Greater prolonged engagement with happy faces was related to less resistance to changes in happiness (i.e., less happiness inertia), whereas greater prolonged engagement with angry faces associated with more resistance to changes in anger (i.e., greater anger inertia). Results suggest that socioemotional processes captured by laboratory measures have real-world implications for adolescent affective states and highlight potentially actionable targets for novel treatment approaches (e.g., just-in-time interventions). Future studies should continue to assess relations among socioemotional informational processes and dynamic fluctuations in adolescent affective states.

Pleasure and Satisfaction as Predictors of Future Cigarette and E-Cigarette Use: A Novel Two-Stage Modeling Approach.

INTRODUCTION: Subjective experience of e-cigarettes may be an important factor in helping people who use combustible cigarettes switch completely to e-cigarettes to reduce harms from smoking. This paper describes a novel two-stage analysis using pleasure and satisfaction responses from Ecological Momentary Assessments (EMA) of both cigarette and e-cigarette use to predict future cigarette and e-cigarette tobacco use. METHODS: This observational study included adult users of cigarettes and e-cigarettes who provided 7-days of EMA, capturing cigarette and e-cigarette use, followed by biweekly reports of cigarette and e-cigarette use over one year. Participants were 279 adults who provided both cigarette and e-cigarette responses during the EMA. We employed a two-stage analytic approach in which EMA data were used to predict subsequent levels of cigarette and e-cigarette use. In the first stage, EMA responses of cigarette and e-cigarette events were modeled via a mixed-effects location scale (MELS) model to yield summaries of participants' means and variability on event-related ratings of pleasure and satisfaction. These EMA summaries served as predictors in the second stage analysis of the biweekly post-EMA longitudinal cigarette and e-cigarette use data. RESULTS: EMA pleasure and satisfaction ratings were similar for both products and predicted both longitudinal cigarette and e-cigarette use, even after controlling for baseline cigarette and e-cigarette dependence. Relatively higher levels of satisfaction with e-cigarettes were associated with greater decreases in cigarette use over time. CONCLUSIONS: Pleasure and satisfaction are important predictors of subsequent cigarette and e-cigarette use. IMPLICATIONS: Experienced subjective pleasure and satisfaction from e-cigarettes relative to cigarettes may be an important factor in helping individuals who smoke to switch completely to e-cigarettes as a harm reduction approach. In order to help sustain complete product switching and reduce dual use or relapse to smoking, e-cigarettes may need to deliver more satisfaction to the user compared to that experienced from cigarettes.

The misjudgment of interoceptive awareness: Systematic overrating of interoceptive awareness among individuals with lower interoceptive metacognitive skills.

Knowing when perceiving inner bodily signals better and when perceiving them worse is a health relevant but understudied dimension of interoception. Therefore, the present study assessed interoceptive metacognition (IMC) as the skill to adequately monitor interoceptive accuracy in the cardiac domain. We used the Graz Ambulatory Interoception task (GRAIT), which applied two intervals of the heartbeat tracking task 12 times a day for 3 days in total to n = 66 participants. We assessed IMC as the relative correspondence between interoceptive accuracy and the subjective confidence ratings. We found that 6 % of the total IMC variance was due to person, which was assessed reliable (RKRn=0.81). Furthermore, the between-person variation of IMC was negatively associated with the MAIA (especially attention regulation and self-regulation). People who believe that they are aware of their interoceptive experiences (MAIA) showed lower IMC. This study advocates the assessment of interoception in everyday life.

The European landscape for gene therapies in orphan diseases: 6-year experience with the EMA Committee for Orphan Medicinal Products.

In 2000, the European Union (EU) introduced the orphan pharmaceutical legislation to incentivize the development of medicinal products for rare diseases. The Committee for Orphan Medicinal Products (COMP), the European Medicines Agency committee responsible for evaluation of applications for orphan designation (OD), received an increasing flow of applications in the field of gene therapies over the last years. Here, the COMP has conducted a descriptive analysis of applications regarding gene therapies in non-oncological rare diseases, with respect to (a) targeted conditions and their rarity, (b) characteristics of the gene therapy products proposed for OD, with a focus on the type of vector used, and (c) regulatory aspects pertaining to the type of sponsor and development, by examining the use of available frameworks offered in the EU such as protocol assistance and PRIME. It was noted that gene therapies are being developed by sponsors from different backgrounds. Most conditions being targeted are monogenic, the most common being lysosomal disorders, and with a very low prevalence. Generally, adeno-associated viral vectors were being used to deliver the transgene. Finally, sponsors are not frequently using the incentives that may support the development and the reasons for this are unclear.

Childhood abuse and neglect and profiles of adult emotion dynamics.

Childhood maltreatment (CM) is experienced by ∼40% of all children at major personal and societal costs. The divergent associations between emotional, physical, and sexual abuse or neglect in childhood and differences in adult emotional functioning and regulation were examined in terms of daily emotion intensity, variability, instability, inertia, and diversity, reported over 30 days by 290 Dutch aged 19-73. Participants described their abuse/neglect experiences retrospectively using the Childhood Trauma Questionnaire (CTQ). Dissecting CM effects on adult emotion dynamics may inform theories on the ontogenesis and functioning of emotions, on effects of abuse and neglect, to better understand (dys)functional emotional development, and to prevent their adverse sequelae. Structural equation models (SEM) showed that most types of CM were associated with specific patterns of emotion dynamics, and only emotional abuse had no unique effects on the emotional dynamic indices. Emotional neglect was associated with most measures of emotion dynamics (i.e., less intense, variable, unstable, and diverse emotions). Sexual abuse associated with increases and physical neglect decreases in negative affect variability and instability. Physical abuse was associated with inertia but with a small effect size. Social contact frequency did not mediate much of the relationship between CM types and emotion dynamics.

Multi-level prediction of substance use: Interaction of white matter integrity, resting-state connectivity and inhibitory control measured repeatedly in every-day life.

Substance use disorders are characterized by inhibition deficits related to disrupted connectivity in white matter pathways, leading via interaction to difficulties in resisting substance use. By combining neuroimaging with smartphone-based ecological momentary assessment (EMA), we questioned how biomarkers moderate inhibition deficits to predict use. Thus, we aimed to assess white matter integrity interaction with everyday inhibition deficits and related resting-state network connectivity to identify multi-dimensional predictors of substance use. Thirty-eight patients treated for alcohol, cannabis or tobacco use disorder completed 1 week of EMA to report substance use five times and complete Stroop inhibition testing twice daily. Before EMA tracking, participants underwent resting state functional MRI and diffusion tensor imaging (DTI) scanning. Regression analyses were conducted between mean Stroop performances and whole-brain fractional anisotropy (FA) in white matter. Moderation testing was conducted between mean FA within significant clusters as moderator and the link between momentary Stroop performance and use as outcome. Predictions between FA and resting-state connectivity strength in known inhibition-related networks were assessed using mixed modelling. Higher FA values in the anterior corpus callosum and bilateral anterior corona radiata predicted higher mean Stroop performance during the EMA week and stronger functional connectivity in occipital-frontal-cerebellar regions. Integrity in these regions moderated the link between inhibitory control and substance use, whereby stronger inhibition was predictive of the lowest probability of use for the highest FA values. In conclusion, compromised white matter structural integrity in anterior brain systems appears to underlie impairment in inhibitory control functional networks and compromised ability to refrain from substance use.

The ReCoDe addiction research consortium: Losing and regaining control over drug intake-Findings and future perspectives.

Substance use disorders (SUDs) are seen as a continuum ranging from goal-directed and hedonic drug use to loss of control over drug intake with aversive consequences for mental and physical health and social functioning. The main goals of our interdisciplinary German collaborative research centre on Losing and Regaining Control over Drug Intake (ReCoDe) are (i) to study triggers (drug cues, stressors, drug priming) and modifying factors (age, gender, physical activity, cognitive functions, childhood adversity, social factors, such as loneliness and social contact/interaction) that longitudinally modulate the trajectories of losing and regaining control over drug consumption under real-life conditions. (ii) To study underlying behavioural, cognitive and neurobiological mechanisms of disease trajectories and drug-related behaviours and (iii) to provide non-invasive mechanism-based interventions. These goals are achieved by: (A) using innovative mHealth (mobile health) tools to longitudinally monitor the effects of triggers and modifying factors on drug consumption patterns in real life in a cohort of 900 patients with alcohol use disorder. This approach will be complemented by animal models of addiction with 24/7 automated behavioural monitoring across an entire disease trajectory; i.e. from a naïve state to a drug-taking state to an addiction or resilience-like state. (B) The identification and, if applicable, computational modelling of key molecular, neurobiological and psychological mechanisms (e.g., reduced cognitive flexibility) mediating the effects of such triggers and modifying factors on disease trajectories. (C) Developing and testing non-invasive interventions (e.g., Just-In-Time-Adaptive-Interventions (JITAIs), various non-invasive brain stimulations (NIBS), individualized physical activity) that specifically target the underlying mechanisms for regaining control over drug intake. Here, we will report on the most important results of the first funding period and outline our future research strategy.

A large-scale study of stress, emotions, and blood pressure in daily life using a digital platform.

Stress is often associated with pathophysiologic responses, like blood pressure (BP) reactivity, which when experienced repeatedly may be one pathway through which stress leads to poor physical health. Previous laboratory and field studies linking stress to physiological measures are limited by small samples, narrow demographics, and artificial stress manipulations, whereas large-scale studies often do not capture measures like BP reactivity in daily life. We examined perceived stress, emotions, heart rate, and BP during daily life using a 3-wk app-based study. We confirmed the validity of a smartphone-based optic sensor to measure BP and then analyzed data from more than 330,000 daily responses from over 20,000 people. Stress was conceptualized as the ratio of situational demands relative to individual resources to cope. We found that greater demands were associated with higher BP reactivity, but critically, the ratio of demands relative to resources improved prediction of BP changes. When demands were higher and resources were lower, there was higher BP reactivity. Additionally, older adults showed greater concordance between self-reported stress and physiologic responses than younger adults. We also observed that physiologic reactivity was associated with current emotional state, and both valence and arousal mattered. For example, BP increased with high-arousal negative emotions (e.g., anger) and decreased with low-arousal positive emotions (e.g., contentment). Taken together, this work underscores the potential for expanding stress science and public health data using handheld phones to reliably and validly measure physiologic responses linked to stress, emotion, and physical health.

Ecological momentary assessment of physical activity and sedentary behaviour in shift workers and non-shift workers: Validation study.

This study examined the criterion validity of an ecological momentary assessment (EMA)-reported physical activity and sedentary time compared with accelerometry in shift workers and non-shift workers. Australian workers (n = 102) received prompts through a mobile EMA app and wore the Actigraph accelerometer on the right hip for 7-10 days. Participants received five EMA prompts per day at 3-hour intervals on their mobile phones. EMA prompts sent to shift workers (SW-T) were tailored according to their work schedule. Non-shift workers (NSW-S) received prompts at standardised times. To assess criterion validity, the association of EMA-reported activities and the Actigraph accelerometer activity counts and number of steps were used. Participants were 36 ± 11 years and 58% were female. On occasions where participants reported physical activity, acceleration counts per minute (CPM) and steps were significantly higher (ß = 1184 CPM, CI 95%: 1034, 1334; ß = 20.9 steps, CI 95%: 18.2, 23.6) than each of the other EMA activities. Acceleration counts and steps were lower when sitting was reported than when no sitting was reported by EMA. Our study showed that EMA-reported physical activity and sedentary time was significantly associated with accelerometer-derived data. Therefore, EMA can be considered to assess shift workers' movement-related behaviours with accelerometers to provide rich contextual data.

RSDNet: A New Multiscale Rail Surface Defect Detection Model.

The rapid and accurate identification of rail surface defects is critical to the maintenance and operational safety of the rail. For the problems of large-scale differences in rail surface defects and many small-scale defects, this paper proposes a rail surface defect detection algorithm, RSDNet (Rail Surface Defect Detection Net), with YOLOv8n as the baseline model. Firstly, the CDConv (Cascade Dilated Convolution) module is designed to realize multi-scale convolution by cascading the cavity convolution with different cavity rates. The CDConv is embedded into the backbone network to gather earlier defect local characteristics and contextual data. Secondly, the feature fusion method of Head is optimized based on BiFPN (Bi-directional Feature Pyramids Network) to fuse more layers of feature information and improve the utilization of original information. Finally, the EMA (Efficient Multi-Scale Attention) attention module is introduced to enhance the network's attention to defect information. The experiments are conducted on the RSDDs dataset, and the experimental results show that the RSDNet algorithm achieves a mAP of 95.4% for rail surface defect detection, which is 4.6% higher than the original YOLOv8n. This study provides an effective technical means for rail surface defect detection that has certain engineering applications.

Personalized Machine Learning-Based Prediction of Wellbeing and Empathy in Healthcare Professionals.

Healthcare professionals are known to suffer from workplace stress and burnout, which can negatively affect their empathy for patients and quality of care. While existing research has identified factors associated with wellbeing and empathy in healthcare professionals, these efforts are typically focused on the group level, ignoring potentially important individual differences and implications for individualized intervention approaches. In the current study, we implemented N-of-1 personalized machine learning (PML) to predict wellbeing and empathy in healthcare professionals at the individual level, leveraging ecological momentary assessments (EMAs) and smartwatch wearable data. A total of 47 mood and lifestyle feature variables (relating to sleep, diet, exercise, and social connections) were collected daily for up to three months followed by applying eight supervised machine learning (ML) models in a PML pipeline to predict wellbeing and empathy separately. Predictive insight into the model architecture was obtained using Shapley statistics for each of the best-fit personalized models, ranking the importance of each feature for each participant. The best-fit model and top features varied across participants, with anxious mood (13/19) and depressed mood (10/19) being the top predictors in most models. Social connection was a top predictor for wellbeing in 9/12 participants but not for empathy models (1/7). Additionally, empathy and wellbeing were the top predictors of each other in 64% of cases. These findings highlight shared and individual features of wellbeing and empathy in healthcare professionals and suggest that a one-size-fits-all approach to addressing modifiable factors to improve wellbeing and empathy will likely be suboptimal. In the future, such personalized models may serve as actionable insights for healthcare professionals that lead to increased wellness and quality of patient care.

Effects of videoconferencing use on momentary changes in disordered eating urges, body dissatisfaction, and mood.

The coronavirus disease 2019 (COVID-19) pandemic led to a global surge in videoconferencing use for work/study-related reasons. Although these platforms heighten exposure to one's image, the implications of videoconferencing use on body image and eating concerns remain scantly examined. This study sought to investigate, in an Australian sample, whether videoconferencing for work/study-related reasons predicted increases in body dissatisfaction (BD), urge to engage in disordered eating (DE; restrictive eating, exercise, overeating/purging), and negative mood at the state level. Participants (N = 482, 78.8% women, Mage  = 20.5 years [SD = 5.3]) completed baseline demographic measures, accompanied by an ecological momentary assessment (EMA) of videoconferencing for work/study-related reasons, BD, DE urges, and negative mood six times a day for 7 days via a smartphone application. Most participants (n = 429; 89.0%) reported state-based videoconferencing use during the EMA phase. Consistent with expectations, state-based videoconferencing use was associated with an increase in state-level urges to engage in exercise. However, contrary to predictions, state-based videoconferencing use was linked to a decrease in state-level BD at the next assessment point and failed to predict negative mood and urges to engage in restrictive eating or overeating/purging at the state level. Given the simplified measure of videoconferencing use, the current research is considered preliminary and future replication and extension, using more nuanced measures, is warranted.

SEMA3: A free smartphone platform for daily life surveys.

Traditionally, behavioral, social, and health science researchers have relied on global/retrospective survey methods administered cross-sectionally (i.e., on a single occasion) or longitudinally (i.e., on several occasions separated by weeks, months, or years). More recently, social and health scientists have added daily life survey methods (also known as intensive longitudinal methods or ambulatory assessment) to their toolkit. These methods (e.g., daily diaries, experience sampling, ecological momentary assessment) involve dense repeated assessments in everyday settings. To facilitate research using daily life survey methods, we present SEMA3 ( http://www.SEMA3.com ), a platform for designing and administering intensive longitudinal daily life surveys via Android and iOS smartphones. SEMA3 fills an important gap by providing researchers with a free, intuitive, and flexible platform with basic and advanced functionality. In this article, we describe SEMA3's development history and system architecture, provide an overview of how to design a study using SEMA3 and outline its key features, and discuss the platform's limitations and propose directions for future development of SEMA3.

Estimation of model accuracy in CASP15 using the ModFOLDdock server.

In CASP15, there was a greater emphasis on multimeric modeling than in previous experiments, with assembly structures nearly doubling in number (41 up from 22) since the previous round. CASP15 also included a new estimation of model accuracy (EMA) category in recognition of the importance of objective quality assessment (QA) for quaternary structure models. ModFOLDdock is a multimeric model QA server developed by the McGuffin group at the University of Reading, which brings together a range of single-model, clustering, and deep learning methods to form a consensus of approaches. For CASP15, three variants of ModFOLDdock were developed to optimize for the different facets of the quality estimation problem. The standard ModFOLDdock variant produced predicted scores optimized for positive linear correlations with the observed scores. The ModFOLDdockR variant produced predicted scores optimized for ranking, that is, the top-ranked models have the highest accuracy. In addition, the ModFOLDdockS variant used a quasi-single model approach to score each model on an individual basis. The scores from all three variants achieved strongly positive Pearson correlation coefficients with the CASP observed scores (oligo-lDDT) in excess of 0.70, which were maintained across both homomeric and heteromeric model populations. In addition, at least one of the ModFOLDdock variants was consistently ranked in the top two methods across all three EMA categories. Specifically, for overall global fold prediction accuracy, ModFOLDdock placed second and ModFOLDdockR placed third; for overall interface quality prediction accuracy, ModFOLDdockR, ModFOLDdock, and ModFOLDdockS were placed above all other predictor methods, and ModFOLDdockR and ModFOLDdockS were placed second and third respectively for individual residue confidence scores. The ModFOLDdock server is available at: https://www.reading.ac.uk/bioinf/ModFOLDdock/. ModFOLDdock is also available as part of the MultiFOLD docker package: https://hub.docker.com/r/mcguffin/multifold.

NAFLD and NASH biomarker qualification in the LITMUS consortium - Lessons learned.

Biomarkers have the potential to accelerate drug development, as early indicators of improved clinical response, to improve patient safety, and for personalised medicine. However, few have been approved through the biomarker qualification pathways of the regulatory agencies. This paper outlines how biomarkers can accelerate drug development, and reviews the lessons learned by the EU IMI2-funded LITMUS consortium, which has had several interactions with regulatory agencies in both the US and EU regarding biomarker qualification in patients with non-alcoholic fatty liver disease and non-alcoholic steatohepatitis. Sharing knowledge of such interactions with the scientific community is of paramount importance to increase the chances of qualification of relevant biomarkers that may accelerate drug development, and thereby help patients, across disease indications. A qualified biomarker enables a decision to be made that all understand and support in a common framework.

The EMA Assessment of Asciminib for the Treatment of Adult Patients With Philadelphia Chromosome-Positive Chronic Myeloid Leukemia in Chronic Phase Who Were Previously Treated With at Least Two Tyrosine Kinase Inhibitors.

Asciminib is an allosteric high-affinity tyrosine kinase inhibitor (TKI) of the BCR-ABL1 protein kinase. This kinase is translated from the Philadelphia chromosome in chronic myeloid leukemia (CML). Marketing authorization for asciminib was granted on August 25, 2022 by the European Commission. The approved indication was for patients with Philadelphia chromosome-positive CML in the chronic phase which have previously been treated with at least 2 TKIs. Clinical efficacy and safety of asciminib were evaluated in the open-label, randomized, phase III ASCEMBL study. The primary endpoint of this trial was major molecular response (MMR) rate at 24 weeks. A significant difference in MRR rate was shown between the asciminib treated population and the bosutinib control group (25.5% vs. 13.2%, respectively, P = .029). In the asciminib cohort, adverse reactions of at least grade 3 with an incidence ≥ 5% were thrombocytopenia, neutropenia, increased pancreatic enzymes, hypertension, and anemia. The aim of this article is to summarize the scientific review of the application which led to the positive opinion by the European Medicines Agency's Committee for Medicinal Products for Human Use.

Development of a Large-Scale Pathogen Screening Test for the Biosafety Evaluation of Canine Mesenchymal Stem Cells.

BACKGROUND: The action of mesenchymal stem cells (MSCs) is the subject of intense research in the field of regenerative medicine, including their potential use in companion animals, such as dogs. To ensure the safety of canine MSC batches for their application in regenerative medicine, a quality control test must be conducted in accordance with Good Manufacturing Practices (GMP). Based on guidance provided by the European Medicines Agency, this study aimed to develop and validate a highly sensitive and robust, nucleic acid-based test panel for the detection of various canine pathogens. Analytical sensitivity, specificity, amplification efficiency, and linearity were evaluated to ensure robust assessment. Additionally, viable spike-in controls were used to control for optimal nucleic acid extraction. The conventional PCR-based and real-time PCR-based pathogen assays were evaluated in a real-life setting, by direct testing MSC batches. RESULTS: The established nucleic acid-based assays displayed remarkable sensitivity, detecting 100-1 copies/reaction of template DNA. They also exhibited high specificity and efficiency. Moreover, highly effective nucleic acid isolation was confirmed by the sensitive detection of spike-in controls. The detection capacity of our optimized and validated methods was determined by direct pathogen testing of nine MSC batches that displayed unusual phenotypes, such as reduced cell division or other deviating characteristics. Among these MCS batches of uncertain purity, only one tested negative for all pathogens. The direct testing of these samples yielded positive results for important canine pathogens, including tick-borne disease-associated species and viral members of the canine infectious respiratory disease complex (CIRDC). Notably, samples positive for the etiological agents responsible for enteritis (CPV), leptospirosis (Leptospira interrogans), and neosporosis (Neospora caninum) were also identified. Furthermore, we conducted biosafety evaluation of 12 MSC batches intended for therapeutic application. Eleven MSC batches were found to be free of extraneous agents, and only one tested positive for a specific pathogen, namely, canine parvovirus. CONCLUSION: In this study, we established and validated reliable, highly sensitive, and accurate nucleic acid-based testing methods for a broad spectrum of canine pathogens.