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.
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.
BackgroundPropofol is widely used in pediatric anesthesia, but it has been implicated in adverse effects on brain development following repeated early-life exposure. Bag3, a co-chaperone protein involved in proteostasis and the neuronal stress response, may play a critical role in regulating synaptic function during early brain maturation.MethodsNeonatal mice were treated with propofol on postnatal days 5–7. Hippocampal neurogenesis was assessed via BrdU immunofluorescence. Synaptic proteins (PSD95, SNAP25) and Bag3 expression were measured by Western blotting. Behavioral performance in adolescence was evaluated using open-field, elevated plus-maze, Morris water maze, Y-maze, and T-maze tests.ResultsPropofol exposure significantly reduced proliferative activity (BrdU incorporation) in the dentate gyrus and decreased PSD95 and SNAP25 expression in both the cortex and hippocampus. Bag3 expression was markedly upregulated, accompanied by a mild increase in its phosphorylated form. Behaviorally, propofol-treated mice showed anxiety-like behavior and impairments in spatial learning and working memory.ConclusionThese findings suggest that early-life exposure to propofol impairs neurogenesis and synaptic plasticity. This process is temporally associated with the upregulation of stress-responsive co-chaperone BAG3, which precedes the of synaptic protein homeostasis. While causal relationship remains to be established, these findings identifies BAG3 as a candidate correlative marker of anesthetic-induced neurotoxicity and highlights it as a target for future mechanistic studies.
Treatment-resistant depression (TRD) remains a major clinical challenge. Intranasal esketamine, used adjunctively with an oral antidepressant, has been evaluated in randomized trials, but uncertainty persists regarding the magnitude and consistency of benefit, durability, and key harms. This systematic review and meta-analysis included randomized controlled trials comparing intranasal esketamine plus an oral antidepressant versus placebo nasal spray plus the same oral antidepressant in TRD. Acute induction (≈4 weeks) and maintenance randomized-withdrawal phases were analyzed separately. Depression outcomes were assessed primarily using the Montgomery–Åsberg Depression Rating Scale (MADRS), and functional outcomes using the Sheehan Disability Scale (SDS). Two reviewers independently screened studies, extracted data, and assessed risk of bias using RoB 2.0. Random-effects models pooled mean differences (MD) for continuous outcomes, risk ratios (RR) for binary outcomes, and hazard ratios (HR) for relapse prevention. Certainty of evidence was rated using GRADE. From 1,518 records, nine reports representing six unique RCTs (1,836 participants) were included. Four acute induction RCTs (n=937) showed greater symptom reduction at day 28 with esketamine (MADRS MD −2.99, 95% CI −5.10 to −0.89; I²=48.5%). Rapid improvement was evident by day 2 (MD −3.25, 95% CI −4.65 to −1.85). Esketamine increased day-28 response (RR 1.44, 95% CI 1.20–1.74) and remission (RR 1.52, 95% CI 1.20–1.92), corresponding to approximately +154 responders and +106 remitters per 1,000 patients, respectively, based on pooled control risks. Functioning improved (SDS MD −1.70, 95% CI −2.61 to −0.79). Two maintenance randomized-withdrawal RCTs (n=899) demonstrated reduced relapse risk with continued esketamine (HR 0.51, 95% CI 0.42–0.62; I²=0%). In acute induction, esketamine increased any treatment-emergent adverse event (TEAE) (RR 1.37, 95% CI 1.25–1.50) and discontinuation due to adverse events (RR 2.68, 95% CI 1.35–5.29), with notable increases in dissociation (RR 7.33, 95% CI 4.49–11.98) and blood pressure increased events (RR 3.96, 95% CI 2.24–7.01). Maintenance TEAE rates were similar between groups (RR 1.07, 95% CI 0.99–1.17). Intranasal esketamine plus an oral antidepressant provides rapid, modest acute improvement and reduces relapse risk during maintenance among stabilized responders/remitters, but increases acute adverse events, supporting use within supervised care and individualized benefit–risk assessment.
At the 74th American Society for Mass Spectrometry (ASMS) Conference in San Diego, the obvious story was hardware. Vendors showcased faster acquisition, higher sensitivity, alternative fragmentation, spatial workflows, and software ecosystems. New or highlighted platforms and workflows came from Waters, Thermo Fisher Scientific, Sciex, Bruker, Biognosys, and Evosep.
But after several days of talks, posters, hallway conversations, and interviews with senior figures in mass spectrometry (MS)-based proteomics, the deeper story was not simply that instruments are getting better. The field is beginning to look past the instrument. The mass spectrometer is still central, but the question is shifting: what has to happen around it for proteomics to become clinically useful, scalable, trusted, and routine?
Beyond the instrument
Jennifer Van Eyk, PhD, professor of cardiology, biomedical sciences, pathology, and laboratory medicine, and director of the Advanced Clinical Biosystems Research Institute at Cedars-Sinai Health Science University, put it most directly: “I think mass spec is no longer the limitation. We have the sensitivity, the throughput, and the accuracy at discovery and targeted levels.”
Jennifer Van Eyk, PhD [Gustav Ceder]
That is a remarkable statement in a field long defined by instrument performance. Van Eyk was not saying that MS innovation is finished. She pointed to continuing gains in quantitation, protein structure, conformational analysis, post-translational modifications (PTMs), top-down proteomics, and protein dynamics. But for clinical impact, she argued, the next bottlenecks are increasingly sample preparation, data analysis, standardization, harmonization, and quality control.
Joshua Coon, PhD, professor of biomolecular chemistry at the University of Wisconsin-Madison and the Pyle Chair at the Morgridge Institute for Research, saw instrument speed as the force opening new applications. Faster scanning mass analyzers are allowing deeper proteome coverage, more post-translational modification (PTM) measurements, and shorter runs. Ryan Kelly, PhD, professor of chemistry and biochemistry at Brigham Young University, framed the same shift as a throughput problem. “Now the mass spec is so fast that we need to figure out how to feed it faster,” he said. In plasma proteomics, Coon said, faster instruments, nanoparticle-based enrichment, and improved chromatography are moving the field from hundreds
Joshua Coon, PhD [Gustav Ceder]
toward thousands of detectable proteins in blood.
John R. Yates III, PhD, the John Lytton Young Endowed Chair in the department of integrative structural and computational biology at Scripps Research, highlighted electron activation dissociation methods and the possibility that high-throughput workflows could push MS deeper into plasma and population-level studies. He described targeted affinity platforms as powerful for “known knowns” because they measure targets defined in advance. “But with mass spectrometry,” he added, “you can look for unknown unknowns, which is where the gold lies.”
John R. Yates III, PhD [Gustav Ceder]
The point cuts to the heart of where the field now stands, and a recurring ASMS tension. The future of proteomics is not a choice between platforms. It is a division of labor. Targeted affinity technologies have become central to large-scale plasma proteomics and population studies. MS remains uniquely powerful for unbiased discovery, tissue proteomics, complex sample matrices, protein modifications, structural diversity, and biology that is not yet named.
From depth to trust
If the first era of modern proteomics was about seeing more, the next may be about measuring better. Devin Schweppe, PhD, assistant professor in the Department of Genome Sciences at the University of Washington, described the current moment as a “duality.” Instruments can now deliver deep coverage, and computational tools are making interpretation faster. Together, he said, they are creating “a comfort level with trusting the data.”
Devin Schweppe, PhD [Gustav Ceder]
Trust came up repeatedly. For discovery biology, a strong signal can be enough to generate a hypothesis. For clinical practice, it is not. Van Eyk said clinical-grade assays are “way harder than people think they are.” A research study can iterate. A clinical assay has to deliver the same measurement today, in five weeks, in six months, and years later. Once a test is locked, “you can’t go, ‘Oh no, we should have had this extra protein in there,’” she said. “It’s done.”
This distinction matters across assay types. Targeted MS methods such as multiple reaction monitoring (MRM) and parallel reaction monitoring (PRM) can provide absolute quantification, but only for preselected proteins. Data-independent acquisition (DIA), meanwhile, has moved discovery proteomics closer to translation by improving reproducibility and scalability. DIA is still often used for relative quantification, but its ability to capture patterns across tens or hundreds of proteins may become important as clinical decision-making moves beyond single biomarkers and reference intervals.
The field is responding to these demands. David Kotol, PhD, R&D manager at ProteomEdge, discussed an independently validated nine-protein plasma panel designed to improve emergency department triage and imaging decisions for patients with suspected venous thromboembolism, compared with D-dimer alone.
David Kotol, PhD [Gustac Ceder]
Kotol described a shift “from relative protein measurements toward robust, multiplexed absolute quantification.” He emphasized stable isotope-labeled protein standards added early in sample preparation to monitor digestion efficiency, downstream analytical variation, and multi-peptide quantification. These standards cannot remove variation introduced during sample collection, handling, or storage. But they can make the analytical workflow more transparent and transferable.
The clinical gap
Mathieu Lavallée-Adam, PhD, associate professor in the department of biochemistry, microbiology and immunology and director of the specialization in bioinformatics at the University of Ottawa, gave the least glamorous answer to what still blocks clinical translation. “My answer is going to be boring,” he said. “It’s going to be education.”
Mathieu Lavallée-Adam, PhD [Gustav Ceder]
Lavallée-Adam argued that many clinicians and biomedical researchers still do not fully understand what modern MS can do. Too often, the outside view is still: give me a list of differentially expressed proteins. But MS-based proteomics has moved beyond lists, into proteoforms, structural information, PTMs, protein dynamics, and flexible acquisition. “We’re past that now,” he said. “The main barrier is our inability to communicate the possibilities that we offer.”
Sasha Singh, PhD, assistant professor of medicine at Harvard Medical School, associate scientist at Brigham and Women’s Hospital, and director of proteomics research at the Center for Interdisciplinary Cardiovascular Sciences (CICS), described this translation role from inside a hospital environment. “That’s actually my role at the hospital,” Singh said. “I am a liaison between the technology and the application scientist.”
Sasha Singh, PhD [Gustav Ceder]
The translation is becoming harder because proteomics is diversifying. End users often need to distinguish among discovery MS, which can provide broad relative quantification; targeted MS, which can provide absolute concentrations for selected proteins; and targeted affinity proteomics, which can scale well for plasma cohorts but is limited by predefined assays and available binding reagents. Singh added that different technologies may produce profiles that do not fully overlap. Rather than treating that as a failure, she suggested it reveals something real: the circulation contains many subproteomes, and different technologies enrich different views.
AI with guardrails
No 2026 conference escapes artificial intelligence (AI), and ASMS was no exception. But the mood among the researchers was cautious rather than breathless.
Lavallée-Adam said agent-based AI was dominating conversations in his part of the field. The dream is seductive: put a sample on an instrument, ask an AI agent to maximize protein identifications or optimize a method, and let it select the best protocol. But he drew a clear line between potential and reality. “Are such agents really driving change? It’s unclear at this point,” he said. “I think it’s unproven.”
Still, AI-assisted acquisition strategies are entering workflows. Lavallée-Adam’s group works on real-time MS data acquisition, where software analyzes data as it is acquired and adapts the run to the biological question. Instead of measuring the same abundant proteins repeatedly, the system can decide it has seen enough and move on to new targets. In that sense, AI becomes less a magical oracle than an instrument assistant.
Faster instruments are generating more data, and faster analysis is needed to keep up. Schweppe also argued that open-source tools remain essential because they let laboratories build on one another’s work rather than rebuild it.
More than abundance
Much of the clinical proteomics effort is focused on plasma because it is minimally invasive and suitable for screening, longitudinal sampling, and routine monitoring. But even in blood, researchers are learning that plasma is only part of the story.
Roman Fischer, PhD, associate professor and head of the Discovery Proteomics Facility at the Target Discovery Institute, University of Oxford, pushed the conversation back toward biology. Plasma alone does not capture the full circulating system, he noted. Peripheral blood mononuclear cells, extracellular vesicles, microvesicles, and other compartments may contain disease-relevant information that conventional workflows miss. “We have to be more sophisticated in addressing the compartments of the blood,” Fischer said.
Roman Fischer, PhD [Gustav Ceder]
He also pointed to the proteoform problem. A single gene can give rise to many transcripts, isoforms, modified proteins, and glycosylated forms. These differences may affect activity, localization, disease pathways, and therapy response. Capturing that diversity is not possible with targeted affinity assays alone. It requires deeper characterization of the proteome, not only quantification.
Yates offered a clinical example. His group has been developing protein-footprinting approaches that can detect conformational changes in proteins in blood. In one transthyretin amyloid cardiomyopathy project, he said, abundance alone was not the answer. The important signal was how the protein folded or misfolded. That kind of assay moves proteomics beyond proteins going up or down, into structural disease biology.
Van Eyk’s work on remote sampling devices pointed to another future: patient-collected blood samples that make longitudinal cardiovascular studies easier, more inclusive, and better matched to real clinical questions.
In the background was a broader translational arc: discovery, verification, clinical validation, health economics, and access. Plasma proteomics highlights included Lekha Sleno, PhD, professor at Université du Québec à Montréal, who is combining nanoparticle enrichment with isotope-enabled targeted proteomics, and a CinderBio breakfast seminar featuring Fredrik Edfors, PhD, assistant professor at KTH Royal Institute of Technology and SciLifeLab, and Simion Kreimer, PhD, senior research project advisor in the Proteomics and Metabolomics Core at Cedars-Sinai Health Science University.
The seminar focused on accelerated plasma proteomics, rapid digestion workflows, stable isotope standards, Human Protein Atlas resources, and faster enzyme workflows that can reduce lead times. The common message was that sample preparation, quantification, and validation may become as decisive as instrument resolution.
The next bottleneck
ASMS 2026 was not short on technical spectacle. High-resolution instruments, electron-based fragmentation, narrow-window DIA, rapid acquisition, MS imaging, top-down workflows, and AI-enabled software all had their moment. But the most interesting conversations were less about spectacle than maturity.
Proteomics is no longer trying only to prove that it can see more. It is trying to prove that it can measure consistently, explain biology more deeply, support drug development, fit into clinical laboratories, and eventually improve patient decisions.
That means the next bottleneck is distributed across the ecosystem: sample preparation, standards, software, education, reimbursement, clinical menus, regulatory validation, open tools, and the ability to translate technical power into something a clinician can use.
Longer term, integrated proteomics, other omics, imaging, clinical data, and AI may support not only single biomarkers, but interpretable molecular patterns, longitudinal trajectories, and digital-twin-like models of patient biology.
The field spent decades making proteins visible. The next challenge is making proteomic measurements dependable enough to act on.
A growing body of psychological and neuroscientific research suggests that moral injury (MI) involves maladaptive self-referential processing, including disruptions in moral identity, rigid negative self-appraisals, and impaired meaning-making following exposure to potentially morally injurious events (PMIEs). Building on Mindfulness-to-Meaning Theory (MMT), this paper proposes self-transcendence (ST)—a metacognitive state characterized by reduced self-focus, expanded awareness, transcendent affect, and prosocial meaning—as a potential mechanism for MI recovery. Within MMT, mindfulness practice is theorized to cultivate ST via decentering and meta-awareness, processes that broaden attentional scope, promote flexible cognitive reappraisal, and modulate habitual self-referential processing. Mindfulness and contemplative research further link ST to increased cognitive flexibility, reduced in shame-focused narrative self-processing, and adaptive integration of emotionally and morally disruptive experiences. Drawing on an integrative review of ST-consistent and MI-related mechanisms, this paper argues that fostering ST through mindfulness-based and contemplative practices may reduce rigid self-focus, expand interpretive frameworks of meaning, and support moral identity repair and meaning-making. Implications are discussed for designing interventions that intentionally cultivate ST as both standalone approaches and modular components, while acknowledging current limitations in measurement, readiness assessment, and the reliable induction of ST states.
This study applied multivariate ANOVA to investigate age-related microstructural changes in the brain tissues driven primarily by myelin, iron, and water content, as observed in MRI (semi-)quantitative R1, R2*, MTsat and PD maps. This is effectively a re-analysis of the data analyzed in a univariate way in a previous publication. Voxel-wise analyses were performed on gray matter (GM) and white matter (WM), in addition to region of interest (ROI) analyses. The multivariate approach identified brain regions showing coordinated alterations in multiple tissue properties and demonstrated bidirectional correlations between age and all examined modalities in various brain regions, including the caudate nucleus, putamen, insula, cerebellum, lingual gyri, hippocampus, and olfactory bulb. The multivariate model was more sensitive than univariate analyses, as evidenced by detecting a larger number of significant voxels within clusters in the supplementary motor area, frontal cortex, hippocampus, amygdala, occipital cortex, and cerebellum bilaterally. Though when cross validating the results by splitting the data into 2 subsets, sensitivity is strongly reduced, even more so for the multivariate approach. The examination of normalized, smoothed, and z-transformed maps within the ROIs revealed concurrent age-dependent alterations in myelin, iron, and water content. These findings contribute to our understanding of age-related brain differences and provide insights into the underlying mechanisms of aging. The study emphasizes the importance of multivariate analysis for detecting subtle microstructural changes associated with aging when dealing with multiple quantitative MRI parameter maps.
Background: Difficulty understanding speech in noisy environments is a primary challenge of hearing impairment, inadequately addressed by hearing aids alone. While auditory training can enhance selective attention and speech perception, current digital programs face poor user adherence and lack realistic 3D spatial audio. Objective: This pilot study evaluated the feasibility, usability, and preliminary efficacy of ARIA (Augmented Reality Immersive Auditory training), a handheld mobile intervention that provides gamified at-home auditory training to middle-aged adults via earbud-delivered spatial audio. Methods: In this single-arm, pre-post–follow-up pilot study, 11 adults (mean age 53.0, SD 3.0 y) with functional hearing not requiring amplification completed a 4-week at-home training program using ARIA on provided devices (iPhone 14 Pro, AirPods Pro 2). Speech-in-noise perception was assessed via the Korean Matrix Sentence Test at baseline, 4 weeks, and 8 weeks at 3 signal-to-noise ratios (SNRs; 0 dB, −6 dB, and −9 dB, respectively). Feasibility, usability (System Usability Scale), user experience (Player Experience of Need Satisfaction), in-game performance, and qualitative feedback were collected. Results: Protocol completion was 100% (11/11), demonstrating technical feasibility. Exploratory efficacy analyses revealed statistically significant speech-in-noise improvements posttraining across all conditions (0 dB: =3.43, =.02; −6 dB: =5.34, <.001; −9 dB: =4.34=.004). Gains were maintained at the 8-week follow-up. In-game localization improvements correlated significantly with speech perception gains at −6 dB SNR (ρ=0.639; =.03) and −9 dB SNR (ρ=0.612; =.045). User experience showed mixed results: the mean System Usability Scale score was 70.2 (SD 19.6; range 47.5‐92.5), reflecting substantial individual differences in usability perception. While 72% (n=8) reported difficulties with the augmented reality (AR) environmental setup, 63% reported genuine mastery-driven engagement with core gameplay. Thematic analysis revealed a dissociation between peripheral usability challenges (setup friction, “homework” characterization due to protocol structure) and successful engagement with the training paradigm itself. Conclusions: This pilot demonstrated the feasibility of AR-based audio-motor training for at-home delivery and revealed encouraging preliminary efficacy signals, warranting progression to controlled efficacy trials. Formative findings identified specific usability refinements needed for broader implementation, particularly streamlining AR setup while preserving the core gameplay elements that successfully fostered competence and engagement. These insights provide clear guidance for platform optimization and randomized controlled trial design.
Introduction and objectivePopulations at high altitude (HA) face a higher incidence and severity of traumatic brain injury (TBI). This pilot study utilized longitudinal 1H-MRS to identify neurochemical biomarkers of HA adaptation and the subsequent metabolic response to mild TBI (mTBI).MethodsMale C57BL/6J mice were exposed to simulated HA (5,000 m) or sea level (SL) for 12 weeks. Following adaptation, a unilateral mTBI was induced via closed head injury (CHI). Mice were then monitored for an additional 2 weeks at HA (total duration of 14 weeks). In vivo1H-MRS spectra (7 T) were collected from the frontal cortex, hippocampi, and cerebellum at weeks 0, 4, 12 to assess HA adaptation. Following the CHI, subsequent measurements were collected at week 12 (post-injury) and week 14 to monitor longitudinal neurochemical responses to the mTBI.ResultsChronic HA exposure induced significant reductions in myo-inositol (Ins) and total choline (tCho) in the hippocampus, establishing a baseline of metabolic fragility that sensitized the brain to subsequent traumatic insult. Post-mTBI, the HA group exhibited a profound “metabolic crisis,” characterized by significantly lower tCho and failed recovery of total N-acetylaspartate (tNAA) compared to SL controls. Total creatine (tCr) was the most acutely affected metabolite, underscoring a depletion of the bioenergetic reserve.ConclusionChronic hypobaric hypoxia fundamentally alters baseline brain metabolism and impairs the neurochemical recovery from mTBI. These findings suggest that standard recovery protocols may be insufficient for HA-adapted populations and highlight 1H-MRS as a critical tool for detecting “invisible” metabolic vulnerability in extreme environments.
Steroid receptor coactivator-1 (SRC-1), also known as nuclear receptor coactivator-1 (NCOA1), represents the first identified member of the p160 nuclear receptor coactivator family and plays a pivotal role in integrating steroid hormone signals, regulating gene transcription, and maintaining neural homeostasis in the central nervous system (CNS). SRC-1 exhibits region-specific, cell-type-specific, and sexually dimorphic expression patterns in the brain, with prominent distribution in key regions including the hippocampus, cerebral cortex, hypothalamus, and amygdala. Functional studies demonstrate that SRC-1 participates in diverse neural functions such as learning and memory, energy metabolism, emotional regulation, and reproductive behavior through modulation of synaptic plasticity-related genes, neurotrophic factors, and metabolic pathways. Aberrant SRC-1 expression is closely associated with neurodegenerative diseases, autism spectrum disorders, and glioblastoma. This review systematically summarizes the molecular structure, expression characteristics, physiological functions of SRC-1, and its roles in neurological disorders, while discussing its potential applications as a diagnostic biomarker and therapeutic target.
This perspective article argues that the theoretical landscape of nonsuicidal self-injury (NSSI) has long been stabilized by the “hydraulic” model of Automatic Negative Reinforcement, which conceptualizes self-harm primarily as a mechanism to down-regulate aversive hyper-arousal. While this framework successfully elucidates the etiology of self-injury driven by high-intensity negative affect, it fails to account for a substantial, treatment-resistant phenotype: adolescents driven by profound anhedonia and ventral striatal hypofunction. This perspective article argues for the formal recognition of an “Anhedonic Subtype” of NSSI. Synthesizing recent epidemiological data identifying “emptiness” as a central symptom network bridge, alongside neurobiological evidence of reward blunting, we posit that for this subtype, NSSI functions not as a sedative, but as a mechanism of “forced activation.” We propose a preliminary differential diagnostic framework distinguishing defensive dissociation from anhedonic deficit and outline the theoretical rationale for exploring a shift in clinical intervention from distress tolerance toward positive affect up-regulation. The clinical utility of this framework remains to be evaluated in future empirical research.
