Dubravka Svob Strac

Background/Objectives: Pregnancy is associated with different biological, physiological, psychological and social changes, and often with depressive and anxiety symptoms. Brain-derived neurotrophic factor (BDNF) has a major role in neuroplasticity, stress re-sponses, cognition, and various psychiatric disorders including depression and anxiety. The results on the relationship between BDNF and depressive or anxiety symptoms in pregnancy are inconsistent. Study aimed to evaluate the association between BDNF with presence and severity of depressive and anxiety symptoms in pregnant women. Methods: Study included 187 medication free, age-matched women without diagnosis of psychiat-ric disorder: 88 women in their 37th week of pregnancy and 99 control nonpregnant women. Depressive and anxiety symptoms were evaluated using the Beck Depression In-ventory (BDI) and State-Trait Anxiety Inventory (STAI) subdivided into STAI-State anxiety and STAI-Trait anxiety. Plasma BDNF was determined by ELISA. Results: Pregnant women had significantly lower plasma BDNF concentrations and a significantly higher BDI and STAI scores compared to controls. Higher trait anxiety symptoms were related to significantly higher plasma BDNF concentration in pregnant women. No significant as-sociation was found between plasma BDNF concentration and the severity of depressive symptoms, or the severity of state anxiety symptoms. Conclusion: This study confirmed reduced BDNF concentration, and more pronounced depressive and anxious symptoms in pregnant vs. nonpregnant women. Depressive symptom’s severity was not related to plasma BDNF concentration in pregnant women. Positive relationship between BDNF and trait anxiety symptoms in pregnancy should be confirmed in larger samples, but could indicate vulnerability to the possible development of postnatal mental disorders.

Background/Objectives: Healthcare staff, especially nurses, face high occupational stress, yet objective biological indicators that complement self-report are not well established. We examined whether metabolic, hormonal and physiological markers act as indicators of stress and relate to anxiety, depression and burnout in nurses and other healthcare workers. Methods: We conducted a cross-sectional study of 300 hospital employees (239 in healthcare, of whom 123 were nurses; 61 non-healthcare) at a Croatian university hospital; of 329 completed questionnaires, 300 (91.2%) met the inclusion criteria and were analysed. Fasting glucose, lipids, thyroid hormones, cortisol, heart rate and blood pressure were measured; psychological status was measured using the 42-item Depression Anxiety Stress Scales (DASS-42), Maslach Burnout Inventory and Zung scale. Analyses used Mann–Whitney and Kruskal–Wallis tests, Spearman correlation, hierarchical logistic regression adjusted for age, sex, smoking and thyroid medication use, and receiver operating characteristic (ROC) analysis. Results: Healthcare workers had more favourable glucose, total and low-density lipoprotein (LDL) cholesterol than the older non-healthcare group (all p ≤ 0.003); because the groups differed substantially in age, these unadjusted differences are descriptive only. Metabolic markers correlated only weakly with distress and burnout. Within healthcare workers, elevated stress was discriminated in univariable analysis by low high-density lipoprotein (HDL) cholesterol (area under the curve (AUC) 0.72), raised triglycerides (AUC 0.67) and glucose (AUC 0.66); however, after adjustment for age, sex, smoking and thyroid medication use, none of the markers remained independently associated with elevated stress. Conclusions: In this relatively healthy hospital workforce, routinely available biological markers showed limited value as standalone indicators of occupational stress; they are exploratory rather than validated biomarkers, and require confirmation in larger, prospective, multicentre studies before they can complement—rather than replace—psychological screening in occupational-health practice.

Mitochondrial dysfunction is increasingly recognized as a major contributor to central nervous system (CNS) disorders, including neurodegenerative and neuropsychiatric diseases. Animal models are essential for elucidating disease mechanisms and supporting the development of new therapeutic strategies. Among these models, non-mammalian organisms offer distinct advantages, including low cost, rapid life cycles, genetic tractability, and suitability for large-scale, high-throughput studies. Organisms such as Saccharomyces cerevisiae, Dictyostelium discoideum, Caenorhabditis elegans, Drosophila melanogaster, and Danio rerio have substantially advanced the understanding of mitochondrial processes relevant to CNS pathology. Studies using these models have revealed conserved mechanisms involving mitophagy, mitochondrial quality control, respiratory function, bioenergetic signaling, and neurodegenerative pathways. Their strengths, including scalability, live imaging capacity, and efficient genetic manipulation, have accelerated disease modeling and therapeutic discovery. However, simplified physiology, evolutionary distance from humans, and the incomplete representation of complex CNS organization limit their translational relevance and often require validation in higher-order organisms. Nevertheless, integrating these models into CNS research, particularly alongside emerging technologies, provides a powerful strategy for linking fundamental mitochondrial biology with translational neuroscience. This review summarizes the use of non-mammalian models in neuroscience research, with an emphasis on mitochondrial dysfunction in CNS disorders and their potential to support future therapeutic advances.

Background/Objectives: Healthcare professionals face high occupational stress, emotional burden, and demanding conditions that increase the risk of burnout, depression, and anxiety, with recent post-pandemic evidence indicating a high and rising global prevalence. Early identification of distress is therefore essential. This review compared three widely used instruments—the Depression, Anxiety and Stress Scales (DASS), the Maslach Burnout Inventory (MBI), and the Zung Self-Rating Depression Scale (SDS)—selected because they jointly cover depression, anxiety, stress, and burnout, unlike unidimensional tools (PHQ-9, GAD-7, PSS, CBI) that would need combining for comparable coverage. Methods: A narrative integrative review searched PubMed, Scopus, Web of Science, ScienceDirect, and Google Scholar for peer-reviewed articles (English/Croatian) published up to December 2025 (final search: 26 June 2026). Two reviewers independently screened records, resolving disagreements by consensus; quality was appraised narratively. Of 55 records, 35 were included. Results: The DASS showed a replicated three-factor structure and strong reliability, with shortened forms (DASS-8, DASS-12) enabling rapid screening at reduced domain coverage. The MBI remained the most widely used burnout measure but was limited by its emotional-exhaustion focus, proprietary licensing, and scoring heterogeneity. The Zung SDS offered a brief depression screen, though dated wording and context-dependent cut-offs favor initial-screen use. Notably, much evidence is derived from non-healthcare populations—the principal limitation. Conclusions: The instruments appear complementary rather than interchangeable; their combined use is proposed—as a reasonable suggestion from the literature, not an empirically demonstrated finding—as a multidimensional approach, contingent on context, objective, and appropriate cultural validation.

Major depressive disorder (MDD) is associated with complex metabolic alterations. In this study, we applied a multiplatform metabolomics approach (GC-MS and LC-MS) to characterize the plasma extracellular vesicle (EV) metabolome in healthy controls (N = 50), responsive MDD patients (N = 60), and patients with treatment-resistant depression (TRD; N = 65). Longitudinal analyses were performed following 8-week treatment with duloxetine (N = 30), bright-light therapy (BLT; N = 30), or esketamine (N = 35). A total of 230 metabolites were identified, with the most pronounced metabolic alterations observed in TRD patients, particularly in lipid, amino acid, and energy metabolism pathways. Elevated lysophospholipids and fatty acids in TRD suggested dysregulated lipid metabolism and inflammatory processes. All treatments resulted in clinical improvement, accompanied by partial normalization of metabolic profiles. Duloxetine treatment was associated with modulation of amino acid and glycerophospholipid metabolism, including increases in tryptophan-related metabolites and normalization of specific lipid species. BLT primarily reduced lysophospholipids and mannose levels, while esketamine modulated metabolites related to lipid turnover, short-chain fatty acids, carbohydrate metabolism, and neuroendocrine function, including increased thyrotropin-releasing hormone levels. These findings support the concept that TRD represents a biologically distinct and more metabolically dysregulated subtype of depression and highlight EV-based metabolomics as a promising approach for elucidating disease and treatment mechanisms.

Introduction: Alzheimer’s disease (AD) is a multifactorial neurodegenerative disorder characterized by predominant - but not exclusive - pathological accumulation of amyloid-β (Aβ) in the brain. This process affects not only neurons (particularly cholinergic) but also glial cells, contributing to progressive neuronal loss and neuroinflammation. Dehydroepiandrosterone (DHEA) and dehydroepiandrosterone sulfate (DHEAS) are endogenous steroids that are hypothesized to exert neuroprotective and anti-inflammatory effects. This study aims to histologically characterize the in vivo temporal progression of Aβ-induced alterations in cholinergic neurons and glial morphology. Our secondary aim was to evaluate whether DHEAS protects cholinergic integrity and, if so, whether this effect is mediated through glial activation. Methods: Aβ _1–42 was injected into the cholinergic nucleus basalis magnocellularis (NBM) region of C57BL6/J male mice and one hour later 10 mg/kg DHEAS or vehicle (0.9% saline) was applied intraperitoneally. After 3, 12 or 33 days, the mice were transcardially perfused and immunohistochemical staining was used to investigate cholinergic cell (ChAT) and fiber (AChE) loss, as well as microglia (IBA1) and astrocyte (GFAP) morphology. Results: Our findings confirmed that Aβ peptide exerted neurotoxic effects on the cholinergic system and triggered time-dependent activation in both glia cell types. Microglial cells initiated their response by day 3, adopting an amoeboid morphology, whereas delayed astrocytic reactivity was observed between days 3 and 12, demonstrated by increased ramification. DHEAS treatment preserved cholinergic fiber density, without effecting the number of cell bodies and modulated the inflammatory responses of glia cells, by decreasing the area occupied and number of microglia in a time dependent manner. Discussion: Aβ toxicity exerts time-dependent effects on both cholinergic neurons and glia cells, while DHEAS shows therapeutic promise, though its efficacy and exact mechanism require further investigation.

Pharmacogenomics is a crucial area in neuropsychopharmacology, offering a scientific foundation for precision medicine in treating psychiatric disorders, particularly schizophrenia spectrum disorders (SSD). Patients with SSD exhibit significant variability in their response to antipsychotic drugs, ranging from lack of efficacy to severe adverse drug reactions. This variability is largely due to genetic differences affecting drug metabolism (e.g., CYP450 enzymes such as CYP2D6 and CYP1A2) and drug targets (e.g., dopamine D2 and serotonin 2A receptors). For instance, an individual’s metabolizer status can dramatically alter drug concentrations, influencing both therapeutic effectiveness and the risk of side effects, such as weight gain and extrapyramidal symptoms. Recent advancements including multi-gene panels and multi-omics approaches (genomics, epigenomics, and metabolomics) are deepening our understanding of these complex drug responses. While promising case studies and clinical trial data highlight the predictive power of pharmacogenetics, its routine clinical integration faces substantial challenges. These include the need for standardized protocols, more robust real-world evidence, and improved omics literacy among healthcare providers. Overcoming these hurdles through collaborative efforts and technological advancements is essential to fully achieve the potential of pharmacogenomics in personalizing antipsychotic treatment, ultimately leading to improved patient outcomes and more effective mental healthcare.

Brain-derived neurotrophic factor (BDNF) plays a crucial role in cognitive functions and dementia. In individuals with mild cognitive impairment (MCI) and dementia, we have investigated BDNF Val66Met genotype distribution, peripheral BDNF DNA methylation, mRNA and protein levels, and cognitive performance using the Mini-Mental State Examination (MMSE) and Clock Drawing Test (CDT). Lower BDNF_IV1 methylation had predictive value for dementia. Patients with mild-to-moderate dementia had lower levels of BDNF_IV2 methylation, whereas patients with severe dementia had higher levels than the MCI group, while BDNF_IV2 methylation positively correlated with CDT scores. An insignificant decline in BDNF mRNA levels in dementia patients positively correlated with significantly lower BDNF plasma levels, especially pronounced in severe dementia patients. BDNF mRNA and protein levels were positively correlated with CDT and MMSE scores, respectively. BDNF Val66Met polymorphism was associated with methylation of the BDNF_IX amplicon, but not with methylation in BDNF promoters I and IV, peripheral BDNF gene and protein expression, MMSE and CDT scores, or dementia. Methylation at the BDNF Val66Met site was positively correlated with overall BDNF_IX methylation and methylation at 5 BDNF_IX CpG loci but negatively correlated with methylation of BDNF_IV1, BDNF_IV3, and BDNF_I1 amplicons. Further studies should evaluate the translational potential of these peripheral BDNF-based biomarkers for dementia.