Background: Health care workers continue to experience heightened levels of distress and burnout, which contribute to higher levels of job dissatisfaction, turnover intentions, presenteeism, and staffing shortages. Objective: The aim of this study was to examine how participation in the StressPal Frontline: Essential Resilience Self-Care and Burnout Prevention program influenced health care workers’ stress and resilience. The study also sought to identify specific measures of perceived stress and resilience that were most affected by participation in the program and to explore whether pre-and-post differences varied based on participant characteristics. The StressPal Frontline program is a digital resilience intervention specifically developed for health care workers to enhance psychological flexibility and stress resilience. The self-paced training program, designed for approximately a 6-week period, consists of brief modules, follow-up resources, and a peer engagement community. Methods: A pretest-posttest quasi-experimental design was used to assess the effectiveness of the StressPal Frontline program in reducing stress and building resilience among 76 health care workers who voluntarily joined and completed the program. Outcome measures included the Perceived Stress Scale and the Brief Resilience Scale to assess participants’ perceptions of stressful situations and their ability to bounce back from stress. Descriptive statistics, correlation analysis, paired-samples 2-tailed test, and multiple regression analysis were conducted. The paired-samples test was calculated at the scale level and item level to evaluate the statistical significance of pretest and posttest mean differences, and the Cohen statistic was used as a measure of effect size. Statistical analysis for this study was conducted in Excel (Microsoft), SPSS (IBM Corp), and Jamovi (jamovi project). Results: The results indicated a 1.53-point reduction in the Perceived Stress Scale score after participating in the StressPal Frontline program, suggesting a statistically significant decline in average perceived stress due to participation in the program (=.004). The corresponding value of Cohen was 0.34, suggesting a small-to-medium effect of the intervention, StressPal Frontline program, in reducing perceived stress. For the Brief Resilience Scale, pre-and-post difference was not significant at the scale level (=.07); however, item-level analysis found significant increases in participants’ perception of their ability to bounce back quickly after hard times and handle difficult situations. No significant differences were found in outcome measures based on age, race, ethnicity, professional role, or practice setting. Conclusions: The StressPal Frontline program was associated with positive outcomes in reducing perceived stress. Our study also found no statistical differences in outcomes among participants of different age groups, races, ethnicities, occupations, genders, and practice settings. This is an important finding, as it indicates that the StressPal Frontline program may provide positive benefits for reducing stress across professions, settings, and individual characteristics. This program, along with other resources, could be implemented by health care organizations to support workers’ professional development, behavioral health, and well-being.
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Astrocytes Preserve Memory Persistence Through Ankyrin-2 Protein in Mice
Although scientists have long studied how memories are formed in the brain, how certain memories persist over time for learning and cognitive function remains unclear.
A new study published in Nature Communications titled, “Astrocytic ankyrin-2 enables memory persistence in the mouse hippocampus,” suggests that astrocytes play a critical role in long-term memory through the regulatory protein ankyrin-2 (Ank2).
Removing Ank2 function led to significantly impaired memory in mice after after two weeks. Under normal conditions, these mice showed standard locomotion, sociability, and recent memory immediately after learning.
Astrocytes lacking Ank2 formed significantly less physical contacts with nearby engram neurons, the specialized neurons for memory storage. Additionally, the maintenance of long-term potentiation (LTP) was impaired while normal synaptic transmission remained intact. The findings suggest that astrocytes stabilize the neural circuits required for preserving memories long after they are formed.
On the molecular level, researchers found that Ank2 is required for brain-derived neurotrophic factor (BDNF) signaling through the astrocytic TrkB.T1 receptor and IP3R2-mediated calcium signaling. In the absence of Ank2, calcium signaling weakened, astrocytes failed to undergo normal structural remodeling, and showed reduced ability to maintain contacts with memory-encoding neurons.
The researchers further demonstrated that hippocampal BDNF infusion normally strengthens long-term memory persistence, but this effect disappeared when astrocytic Ank2 was deleted, showing that Ank2 is essential for BDNF-dependent memory stabilization.
To determine whether astrocytic BDNF signaling alone is sufficient to enhance memory, the team developed an optogenetic tool called Opto-T1. Activation of this pathway promoted astrocyte remodeling, maintained long-term potentiation, and significantly enhanced remote memory without affecting recent memory.
“Our findings show that astrocytes are not passive support cells, but active regulators that determine how long memories last,” said Wuhyun Koh, PhD, senior research fellow at Institute for Basic Science (IBS) and corresponding author of the study. “By identifying Ank2 as a key regulator of astrocyte remodeling and BDNF signaling, we have uncovered a new mechanism that helps stabilize long-term memories and opens new avenues for understanding and potentially treating memory disorders.”
The researchers indicate the study provides a new framework for understanding how astrocytes contribute to neurological diseases.
The post Astrocytes Preserve Memory Persistence Through Ankyrin-2 Protein in Mice appeared first on GEN – Genetic Engineering and Biotechnology News.
Millions of People in Canada Are Finding AI-Enabled Support for Mental Health Effective Amid Ongoing Questions Around Trust.
(OTTAWA) July 8, 2026 — New polling shows approximately six million people in Canada used AI-enabled tools for mental health support in the past year and most find them effective. Today, the Mental Health Commission of Canada (the Commission), in partnership with Mental Health Research Canada (MHRC) and Pollara Strategic Insights, releases the first nationally representative data on how people in Canada engage with digitally supported mental health tools, including AI and virtual care, across every province and demographic.
Quick Facts:
- 1 in 7 people in Canada used AI mental health tools in the past year
- Three out of four who used AI and virtual mental health services found them effective for their well-being
- Only 14 % trust AI tools, just 2% trust them completely
- 40 % of AI users said they were more likely to seek professional care
- Nearly half (45%) who accessed mental health care did so virtually, in whole or in part
WHY IT MATTERS
People in Canada are turning to AI as a convenient way to access mental health support. AI-enabled tools may offer greater convenience and accessibility. Among those surveyed, AI is being used because it is:
- Free or low-cost; 46% of AI users cite this as the reason they use it during a time when financial stress is itself a cause for anxiety.
- Always available; 44% of AI users cite 24/7 access.
- Immediate and convenient; it can be used from anywhere without travelling or waiting for an appointment. For someone in rural Canada, it saves time and travel costs.
- Seemingly private; 39 % of AI service users cite private, anonymous support as a reason for use, while privacy and data protection remain key public concerns.
AI is most used for general well-being (42%), companionship (36%), and mild-to-moderate stress (36%), and 40% of AI users said they were more likely to seek professional care.
WHO IS USING IT AND HOW MUCH DO THEY TRUST IT?
Use is higher among people in Canada under 35 (27%; 29% among men aged 25–34), newcomers to Canada (28%), racialized people in Canada (23%), and 2SLGBTQI+ communities (20%), populations that may experience greater barriers to traditional care.
Overall, trust remains low, particularly for AI-enabled tools, where only 2% of people in Canada trust them completely. People in Canada over 55 show the lowest adoption and trust.
VIRTUAL CARE: EFFECTIVE AND MORE TRUSTED BUT FALLS SHORT OF IN-PERSON SERVICES
45% of people in Canada who used mental health services in the past year did so virtually, with 75% reporting positive outcomes. However, nearly 1 in 3 prefer a hybrid model that combines virtual and in-person services. The data signals what people in Canada need: well-designed tools for safer digital mental health care that they can trust.
THE COMMISSION OFFERS GUIDANCE FOR THE DIGITAL MENTAL HEALTH ERA
The Commission is Canada’s trusted resource for safe digital mental health — assessing apps and tools, setting evidence-based standards, and leading the national conversation on guidance for AI in mental health and substance use health care.
As virtual services and AI-enabled tools continue to expand rapidly across the mental health landscape, there is a growing need for evidence-based insight into how people in Canada engage with, understand, and perceive them. The Commission partnered with MHRC to leverage their ongoing national polling initiative and provide timely insights into usage, attitudes, and concerns related to e-mental health and AI.
The polling is clear: people in Canada want to close the gap between availability and trust. The Commission is working with the Canadian Centre on Substance Use and Addiction and collaborators, provincial governments, technology developers, and health system partners to establish guidance for AI.
“Six million people in Canada have already used AI for mental health support and most found it convenient and effective for their well-being. It is critical that AI is safe and equitable to increase public trust and reduce harms.” – Lili-Anna Pereša, President and Chief Executive Officer, Mental Health Commission of Canada
“The people turning to digitally-supported mental health tools are often those facing some of the greatest barriers to care. Making sure these tools are safe, effective, evidence-based and human-centred is a matter of equity. Ongoing research is essential to understanding where they help and where safeguards are needed.”– Akela Peoples, Chief Executive Officer, Mental Health Research Canada
About Mental Health Commission of Canada
As an independent, not-for-profit with charitable status, the Commission collaborates with leading experts and organizations nationally and internationally, including with people with lived and living experience, to develop national guidelines, standards and strategies, promote innovation and best practices, reduce stigma, increase mental health literacy, and support all levels of government to improve mental health outcomes for everyone living in Canada. The Commission is Canada’s trusted resource for digital mental health best practices with the e-Mental Health Strategy for Canada, app assessment, e-modules for e-mental health implementation, and AI guidance for mental health and substance use health.
About Mental Health Research Canada
As an independent national charity, MHRC works hard to enable a future where mental health in Canada is transformed using evidence, data and stakeholder engagement. We unite researchers, communities, and people with lived experience to bridge gaps in care through national population polling, rapid data reporting, and partnerships that inform policy to improve outcomes. Learn more at www.mhrc.ca
About the Polling
Conducted by Pollara Strategic Insights in partnership with Mental Health Research Canada and the Mental Health Commission of Canada, this national poll (n=3,519) is the first representative data on AI use for mental health in Canada. Full findings: https://mentalhealthcommission.ca/AI-polling-report
About the Funding
The views in this report solely represent the views of the Mental Health Commission of Canada. Production of this report is made possible through financial contribution from Health Canada.
Media Contact
Heather Bakken, Pendulum Group
email: heather@pendulumgroup.ca
cell: 613-406-5432
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Birmingham trusts launch InterSystems shared patient portal
Birmingham and Solihull Mental Health and Birmingham Community Healthcare NHS Foundation Trusts have launched a shared patient portal.
Loneliness From the Digital Mental Health Practitioners’ Perspective: Thematic Analysis of Semistructured Interviews
Background: Loneliness is a prevalent concern across the United Kingdom. While validated scales exist to quantify the severity of loneliness across populations, there remains a gap in understanding how loneliness manifests and is addressed within therapeutic practice. Given the associated stigma surrounding loneliness, practitioner perspectives offer crucial insights into how clients express loneliness within digital therapeutic environments. These insights can inform more nuanced conceptualizations of loneliness. Objective: This study aimed to gather the practitioners’ perspectives on loneliness within a digital therapeutic context and were defined as follows: (1) understand how practitioners identify loneliness concerns, (2) identify how loneliness is elicited in digital mental health interventions, and (3) identify co-occurring themes (such as grief, shame, and social disconnection) that signal loneliness concerns in client communications within digital therapeutic environments. Methods: Semistructured interviews were conducted with 9 practitioners. Participants included specialists in grief counseling, lesbian, gay, bisexual, transgender, and queer or questioning plus support; and digital mental health therapists. Interview transcripts were analyzed using thematic analysis, using an inductive, data-driven approach to allow themes to emerge from participant accounts rather than fitting data to preexisting theoretical frameworks. Results: The following four themes were identified: (1) Conceptualizing Loneliness: practitioners distinguished between social contact and meaningful connection; (2) Contextual Causes: loneliness emerged from life transitions, stigmatized identities, and resource reduction (eg, youth services closures and social support); (3) Expressions and Language: clients rarely expressed loneliness directly, instead using proxy terms, with disclosure patterns varying by age; and (4) Mental Health Co-occurrence: severe mental health conditions created bidirectional cycles of loneliness, exacerbated by symptoms of mental health difficulties. Practitioners reported that many clients experienced loneliness concerns, yet direct disclosure was absent across all participants’ experiences. Conclusions: Practitioners identified multiple stigmatizing experiences as contextual drivers of loneliness, particularly demonstrating how loneliness emerges not only from individual experiences but from broader patterns of social exclusion and marginalization. For therapeutic practice, these insights suggest that practitioners can use awareness of stigmatizing experiences as potential indicators when assessing loneliness risk. The presence of contextual patterns was consistent across practitioners’ experiences, providing a foundation for developing more targeted interventions to address both the emotional experience of loneliness and the underlying social drivers.
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AI Reveals Hidden Brain Lesions in Multiple Sclerosis MRI
It has long been known that brain gray matter plays a key role in multiple sclerosis (MS) disease progression and cognitive impairment, but because magnetic resonance imaging (MRI) has only been able to detect lesions in white matter, neither clinicians nor researchers have had a way to detect or monitor gray matter (cortical) lesions. And while many new drugs developed in the past decade can slow disease progression significantly, they primarily work on reducing white matter lesions.
A University at Buffalo (UB)-led team now reports that it has found a way to use artificial intelligence to reveal these otherwise invisible cortical lesions by reviewing existing MRI scans. The researchers say the significance of finally being able to see what has been known as one of the most important indicators in MS disease progression cannot be overstated.
“Detecting previously invisible cortical lesions on conventional legacy MRI scans has major implications for MS research and clinical care,” commented Robert Zivadinov, MD, PhD, SUNY distinguished professor in the Department of Neurology and director of the Buffalo Neuroimaging Analysis Center (BNAC) in the Jacobs School of Medicine and Biomedical Sciences at UB. “The ability to see for the first time these previously hidden indicators of MS disease progression, including cognitive impairment and disability, is an important advance.”
Added Michael G. Dwyer, PhD, associate professor of neurology and biomedical informatics in the Jacobs School and a researcher with BNAC, “What this collaboration has been able to accomplish is a real success story for applying AI in the medical arena. We now have access to these incredibly useful data on MRI scans that were there but you couldn’t see them without using AI to pull them out. The computational methods are finally at the point where we can do this.”
Zivadinov is senior author, Dwyer first and corresponding author of the team’s published paper in Communications Medicine, titled “Quantifying cortical lesions in multiple sclerosis MRI datasets using multi-contrast post- processing and deep learning.”
“Multiple sclerosis (MS) affects both the inner, connectivity-oriented portions of the brain (white matter) and the outer layer of the brain (the cortex),” the authors explained. While the involvement of cortical lesions in MS has been known almost since the identification of MS in the late 19th century, they weren’t included on diagnostic criteria until the 21st century. And even when they were included, it was noted that their use would be greatly limited due to the current capabilities of clinical MRI.
“Historically, research and clinical care in MS have focused on white matter, where focal demyelinating lesions are a hallmark of the disease,” they continued. And although there are now many therapies that can almost completely halt the incidence of new white-matter lesions in individuals with MS, they haven’t had the same impact on clinical progression, the team continued.
Over more recent decades it’s been found that gray matter is affected from the earliest MS disease stages, and it’s become evident that gray matter pathology is more than secondary to white matter damage. “From a clinical perspective, cortical lesions are strongly associated with clinical disability and cognitive impairment,” the authors stated. “They may also have more prognostic value than white matter lesions for disability and disease course.”
There’s an urgent need for in vivo imaging methods that can show gray matter lesions, they stressed. Dwyer added, “We have all been very frustrated, knowing that these cortical lesions were there but not being able to see them. There’s a lot of ongoing damage that continues to happen in MS that you won’t see with conventional MRI, but that histopathologists have been clearly demonstrating for decades on postmortem tissue.”
For their newly reported study the team applied advanced image processing techniques, including artificial intelligence, to standard MRI scans from a large MS clinical trial. “Recently, several post-processing methods, including synthetic contrasts and artificial intelligence (AI)-based approaches, have shown potential for enhancing cortical lesion detection on conventional MRI data,” they noted. “These methods have the potential to reanalyze existing clinical-trial data to answer key mechanistic questions about both MS development and about treatment effects.”
The AI approaches the researchers used, building on work from co-authors from the Netherlands, were designed to extrapolate vital information from the relationships between multiple images that can’t be seen on a single image.
The researchers combined multiple image-processing techniques, including a new one they developed called MMCLE, or multimodal cortical lesion enhancement. They then applied these techniques to MRI scans from the large, phase III FDA regulatory ORATORIO clinical trial, a study of the MS drug Ocrelizumab that included more than 700 participants.
They found that while individual images of a patient’s brain revealed mostly white matter lesions, once they applied the AI-based image processing methods to multiple different contrast images, they were able to see anywhere from 15 to 20 cortical lesions for each patient, more than 11,000 for the whole dataset. “We confirmed that cortical lesions can be clearly visualized and quantified with these methods,” they stated. “Using deep learning, we also confirmed that the simultaneous use of multiple contrasts improves quantification.”
Dwyer explained further, “If you look on the original scans, you generally can’t see the cortical lesions, but generative AI is very powerful because it can look between the scans and detect tiny differences between them. Because it sees those minor discrepancies, AI can reveal that there’s something going wrong there, that the tissue is not behaving like healthy tissue. The trained models can view multiple MRI images together and synthesize them and synthesize what had been missing.”
Zivadinov added “This work, which has revealed that there is so much invisible pathology in the brain, will have tremendous impact for reviewing data from past clinical trials and also for those going forward,” he says.
The post AI Reveals Hidden Brain Lesions in Multiple Sclerosis MRI appeared first on GEN – Genetic Engineering and Biotechnology News.
Hair cortisol as psychotherapy process parameter – an inpatient pediatric psychosomatic study
IntroductionPediatric-psychosomatic inpatient therapy is an essential part of the German health care system for the treatment of mental disorders in children and adolescents. However, empirical research in this field remains scarce and limited to psychological parameters. This longitudinal naturalistic study aimed to evaluate the efficacy and sustainability of inpatient psychosomatic therapy in children and adolescents by examining both psychological outcomes and biological markers.MethodsA total of 58 patients were assessed at seven time points before, during, and after treatment. Hair cortisol concentration (HCC) was measured as a neuroendocrine parameter of long-term stress regulation. Psychometric data were collected using five validated questionnaires.ResultsFindings indicated significant improvements in perceived stress, depressive and anxiety symptoms, family functioning and internalizing symptoms in the course of inpatient treatment. Overall, these effects remained stable at three- and six-month follow-ups, with only transient increases in depressive symptoms and family problems. HCC showed a significant decrease from admission to discharge and remained stable across follow-ups.DiscussionThese results support the efficacy of inpatient pediatric psychosomatic interventions on both psychological outcomes and neuroendocrine stress regulation and highlight the value of integrating biological markers into psychotherapy research.
Augmented reality-based fine arts education experiences and adolescents’ perceived reduction in depressive symptoms: a self-report study of associations with psychological resilience, rumination, and emotional regulation
As depressive symptoms among adolescents continue to rise, increasing attention has been directed toward educational contexts that may support psychological well-being. Augmented reality, characterized by immersive, interactive, and multisensory affordances, has been increasingly incorporated into fine arts education. However, it remains insufficiently understood how adolescents’ perceived AR-based fine arts education experiences are associated with psychological resilience, emotional regulation, perceived reduction in rumination, and perceived reduction in depressive symptoms. Using post-course self-report questionnaire data from 518 Chinese adolescents, this study tested a cross-sectional associational model with structural equation modeling, without making causal or clinical claims. The empirical results indicate that adolescents’ perceived AR-based fine arts education experiences were positively associated with self-reported perceived reduction in depressive symptoms. Additionally, these experiences were positively associated with self-reported psychological resilience and perceived reduction in rumination. Self-reported psychological resilience and perceived reduction in rumination were positively associated with self-reported emotional regulation, and this regulation was positively associated with self-reported perceived reduction in depressive symptoms. These findings support a correlational psychological model linking perceived AR-based fine arts education experiences with adolescents’ self-reported psychological perceptions and perceived reduction in depressive symptoms. Theoretically, this study contributes to the literature on arts education, educational technology, and adolescent mental health. Practically, the findings suggest that immersive art-learning environments may be perceived by adolescents as supportive contexts for psychological resources. The innovation of this study lies in reframing AR-based fine arts education experiences as psychologically meaningful educational experiences associated with adolescents’ self-reported psychological perceptions
Disrupted self-referential processing and empathy-based interventions in mental disorders: neural mechanisms, cognitive biases, and therapeutic integration
Self-referential processing (SRP), the mental activity of linking experiences and stimuli to the self, is fundamental to shaping identity, regulating affect, and navigating complex social environments. Disruptions in SRP are increasingly recognized as a transdiagnostic hallmark across psychiatric disorders, including schizophrenia spectrum disorders, major depressive disorder, and anxiety disorders. Such disruptions often manifest as distorted self-perception, negative cognitive-affective biases, and dysfunctional connectivity within key neural networks, particularly the default mode network (DMN) and the medial prefrontal cortex (mPFC). Emerging evidence reveals a dynamic bidirectional relationship between SRP and empathy. Impairments in self-processing may hinder the capacity to accurately infer others’ emotional and cognitive states, while deficits in empathic attunement may reinforce maladaptive self-focus. This interplay contributes to social withdrawal, interpersonal dysfunction, and emotional dysregulation across diagnostic boundaries. Recent therapeutic advances suggest that interventions targeting the interface between SRP and empathy may effectively modulate shared neurocognitive mechanisms. Such interventions include metacognitive therapy, mindfulness-based interventions, and psychedelic-assisted psychotherapy. These therapeutic approaches show preliminary but promising evidence for reducing maladaptive self-focused attention, enhancing emotion regulation, and improving social functioning. This review synthesizes empirical evidence regarding the neural underpinnings of SRP, delineates disorder-specific alterations in self-referential processing, and examines the bidirectional interplay between SRP and empathic functioning. We further outline a conceptual framework for empathy-based interventions targeting SRP dysfunction and propose future directions for precision psychiatry research. Emphasis is placed on developing personalized, mechanism-driven therapeutic strategies informed by computational modeling, transdiagnostic biomarker identification, and digital therapeutic platforms.
Biomarkers Could Help in Antidepressant Choice
Antidepression treatment based on a person’s individual biomarkers could help determine which of the world’s most popular medications to use, a clinical trial suggests.
The SMART Trial to Predict Anhedonia Response to Antidepressant Treatment results suggest that behavioral, brain, and clinical data could together determine the optimal antidepressant to choose before a person starts treatment.
There were no significant primary endpoint differences in depression outcomes between participants who received bupropion or sertraline based on biomarkers identified in the prior Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study.
But people negative for biomarkers with both drugs had significantly worse depression symptom trajectories than those who had at least one positive biomarker, regardless of the drug they received.
Response rates among participants with both biomarkers were almost double that of those without any biomarkers, with those who had at least one biomarker having an intermediate response.
“Our results suggest that we could boost response rate by using two sets of biomarkers previously identified in the EMBARC study, making an important contribution to advancing the goals of precision psychiatry,” reported Peter Zhukovsky, PhD, from Harvard Medical School, and co-workers in Nature Mental Health.
“Ultimately, we strongly hope these advances will enable personalized treatment guidance to accelerate and boost antidepressant benefits.”
Treatment for depression is often still trial and error, with symptoms improving in only half the people taking an antidepressant. This could be due to treatments not being chosen based on people’s individual characteristics.
Finding markers that predict response to different antidepressants could therefore provide patients and clinicians with valuable information to guide treatment choice.
The trial was among the first to investigate how treatment could be guided using clinical information such as responses to questionnaires, behavioral information such as performance in computerized tasks, and brain data such as magnetic resonance imaging (MRI) scans.
It was carried out as part of the Wellcome Leap Multi-Channel Psych Program effort to double the number of people who respond to the first treatment they try for depression via the integration of multimodal biomarkers.
Firstly, the researchers investigated biomarker models that predicted response to the selective serotonin reuptake inhibitor (SSRI) sertraline or the norepinephrine-dopamine reuptake inhibitor bupropion using the EMBARC study.
The treatment-assignment algorithm that was developed generated two marker-based indications for each patient—one for bupropion and sertraline—with the predictive model achieving a cross-validated area under the curve of 0.86 and 0.66, respectively.
The team then examined whether antidepressant response could be boosted using their created biomarker combination of a functional MRI imaging marker, reward learning and sensitivity, cognitive control, the clinical variables of depression severity and neuroticism, and the demographic variable of employment status.
After analyzing these biomarkers among participants, who had major depressive disorder, the group was randomly assigned to receive a full 8-week course of an SSRI or non-SSRI.
The primary outcome was the change in depression severity from pretreatment baseline to eight weeks after the start of treatment, with no significant differences in treatment outcomes for those assigned a drug consistent versus inconsistent with their biomarkers.
This, the researchers say was possibly due to the limited power to detect moderate effects.
However, significant differences emerged in symptom reduction trajectories for those with positive markers for both medications, with a response rate of 71.4% compared with 65.4% for those with a positive biomarker for either drug and 42.9% for those with two negative markers.
The authors concluded: “We found that, relative to patients with two negative markers, those with one or two markers were characterized by significantly larger reduction in depressive symptoms, showing that biomarker-guided treatment selection can boost efficacy for two of the most widely prescribed antidepressants around the world.”
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