This review presents a balanced synthesis of the efficacy-nanotoxicity window of nanomaterials-including carbon-based structures, metallic nanoparticles, and bioceramic composites-within the context of stem cell-assisted tissue engineering. It emphasizes that increases in conventional differentiation markers may not necessarily correspond to functional maturity or long-term safety. Therefore, a gradual shift from standard viability-based assays toward lineage-specific functional assessments is discussed. The nanotoxicological profile of these materials is examined across major tissue types, highlighting how similar physicochemical properties may yield beneficial or adverse outcomes depending on concentration, exposure time, and microenvironmental conditions. In bone tissue, the review considers the point at which enhanced mineralization may be accompanied by oxidative stress and mitochondrial strain. For cartilage, potential concerns related to ion and degradation product accumulation in avascular environments are addressed, including the possible induction of inflammatory signaling and hypertrophic markers such as collagen type X. In neural and cardiac applications, efforts to improve electrical conductivity are evaluated alongside potential electrophysiological alterations, including Ca[2+] imbalance and rhythm disturbances at sub-cytotoxic levels. Vascular differentiation is discussed within the context of pro-angiogenic signaling and the risk of endothelial dysfunction. By comparing experimental systems ranging from 2D cultures to induced pluripotent stem cell (iPSC)-derived organoids, the review underscores the model-dependent and tissue-specific nature of nanotoxic responses. Overall, it outlines considerations for integrating physicochemical characterization with functional and metabolic endpoints to support more reliable evaluation of nanobiomaterials in regenerative medicine.

The aquatic biota of the Amazon is subject to considerable daily and seasonal fluctuations in its environment. The ability to adapt to changes in the environment, such as increased water temperatures, is essential for the survival and physiological performance of these organisms. The anticipated rise in temperature and CO2 levels, projected to reach 6 °C and 50% above current levels by the end of the century, respectively, represents a significant and imminent threat to aquatic biodiversity. The objective of this study was to assess the effects of acute short-term exposure to elevated temperature on the brain mitochondrial respiration of two Amazonian fish species with contrasting trophic roles: Schizodon fasciatus, which represents a prey species at a lower trophic level, and Pygocentrus nattereri, a predatory species. Specimens of Schizodon fasciatus and Pygocentrus nattereri were collected and subjected to temperatures of 28 °C and 37 °C for a period of four hours. Brain mitochondrial respiration was quantified to analyze complexes I, I + II, III, IV, proton leak (H[+]Leak), RCR, and ETS. Additionally, the production of reactive oxygen species (ROS) was evaluated. The results revealed that exposure to higher temperatures caused elevated complex I activity in both species, with a more pronounced increase observed in S. fasciatus. Furthermore, significant increases in complex I + II and complex III were observed in S. fasciatus, while complex IV showed a decrease in both species. H[+]Leak exhibited a decrease in P. nattereri, whereas RCR demonstrated an increase in both species. ROS production displayed a decrease in P. nattereri but remained stable in S. fasciatus. The findings suggest that both fish species possess the capacity to adapt their brain mitochondrial function to higher temperatures, albeit with species-specific responses. These adaptations assist in maintaining brain energy production and minimizing oxidative stress, and emphasize potential imbalances in predator-prey relationships under changing environmental conditions.

The Na[+],K[+]-ATPase plays a fundamental role in memory formation by regulating neuronal excitability and NMDA receptor-dependent synaptic plasticity. While this enzyme's dysfunction is broadly linked to cognitive disorders, its specific contribution to fear memory consolidation, particularly within the key brain structure of the basolateral amygdala (BLA), remains undefined. We investigate whether Na[+],K[+]-ATPase inhibition disrupts contextual fear conditioning (CFC) memory consolidation and examine downstream molecular, oxidative, and inflammatory responses in interconnected brain regions. Fifty-day-old male Wistar rats received bilateral cerebral infusions of the Na[+],K[+]-ATPase inhibitor ouabain (1 μm) or vehicle immediately after CFC training. Memory retention was assessed 24 h later. Additional cohorts were euthanized 6-24 h after infusion for biochemical analysis of Na[+],K[+]-ATPase activity, expression of Na[+],K[+]-ATPase isoforms (α1, α2, α3, β1), oxidative stress markers, and pro-inflammatory cytokines in the amygdala, hippocampus, and prefrontal cortex. Post-training Na[+],K[+]-ATPase inhibition in the BLA selectively impaired fear memory consolidation. Ouabain induced changes in Na[+],K[+]-ATPase activity in the hippocampus and prefrontal cortex, and induces time-dependent upregulation of α1 and β1 isoforms. Six hours after infusion, ouabain triggered oxidative imbalance - characterized by reduced CAT activity. Pro-inflammatory cytokine expression was markedly elevated in the hippocampus and prefrontal cortex, while D-serine co-administration failed to rescue memory deficits. These findings establish a previously unrecognized role for Na[+],K[+]-ATPase signaling in the BLA in coordinating the neurobiological processes essential for fear memory consolidation, indicating that its precise regulation is required for adaptive fear memory formation.

Apurinic/apyrimidinic endonuclease 1 (APE1) is a multifunctional protein that occupies a key position at the interface between base excision repair (BER) and cellular redox control during inflammation. As the major AP endonuclease in the BER pathway, APE1 maintains the genomic stability by the repairing oxidative DNA lesions that accumulate in chronically inflamed tissues. In addition, its redox effector factor 1 (Ref-1) activity modulates a broad range of transcription factors, thereby influencing inflammatory cytokine production and the cellular response to redox imbalance. Through this combination of DNA repair and redox-signaling functions, APE1 acts as a central hub that couples oxidative DNA damage to inflammatory signaling networks. Dysregulation of APE1 expression or subcellular distribution has been associated with various inflammation-associated diseases, reflecting its broad impact on inflammatory pathology. This review summarizes current understanding of APE1's dual role in inflammation, highlights opportunities and challenges for therapeutic targeting, and discusses its emerging value in the precision monitoring and management of inflammation‑associated diseases.

A paper-based electrochemical aptasensor (PEA) is reported for the simultaneous detection of extracellular dopamine (DA) and serotonin (SE) in human brain samples, enabling the assessment of neurotransmitter imbalances associated with Alzheimer's disease (AD). The PEA integrates a dual-selectivity strategy that combines aptamer-mediated molecular recognition with potential-resolved electrochemical neurotransmitters discrimination, allowing reliable differentiation between healthy and AD-diagnosed left prefrontal cortex tissues based on altered dopaminergic and serotonergic levels. Thiolated aptamers immobilized onto gold nanoparticles (AuNPs) at the paper electrode interface provide selective neurotransmitter biorecognition, while the distinct oxidation potentials of DA (+ 0.10 V) and SE (+ 0.25 V vs. Ag/AgCl) enable their label-free, simultaneous quantification on a single disposable electrode. The PEA read biologically meaningful concentration shifts across a clinically relevant dynamic range, spanning levels characteristic of healthy tissue to those observed in advanced pathological stages. The PEA approach highlights its potential for liquid biopsy-oriented neurochemical monitoring and supports its application in multiplexed biosensing within organ-on-a-chip systems, enabling early diagnostic strategies for neurodegenerative diseases.

Hair follicle stem cells (HFSCs) are vital for maintaining hair growth and regeneration. Their dysfunction, often caused by oxidative stress or metabolic imbalance, leads to hair loss and scalp aging. β-catenin is a key protein in the Wnt signaling pathway, and its activity is crucial for HFSC maintenance and regeneration. In this study, we show that methylene blue (MB), a mitochondrial-targeted antioxidant, promotes HFSC proliferation and viability by both reducing reactive oxygen species (ROS) and activating the β-catenin signaling pathway. Functional tests demonstrated that MB significantly sped up wound healing in HFSC cultures, indicating enhanced regenerative ability. Although co-treatment with antioxidants like vitamins A and C improved ROS scavenging, it surprisingly reduced MB-induced β-catenin activation. Conversely, minoxidil, a clinically approved hair growth stimulant, synergized with MB to further activate β-catenin signaling and enhance HFSC viability. Lastly, exposure to glucagon-like peptide-1 receptor agonists (GLP-1 RAs) resulted in a dose-dependent decrease in HFSC survival, consistent with emerging reports of treatment-related hair loss. Remarkably, pre-treatment with MB protected HFSCs from GLP-1 RA-induced metabolic stress and premature cell death. Overall, these findings identify methylene blue as a multifunctional therapeutic candidate that reduces oxidative and metabolic stress while supporting HFSC-mediated hair regeneration.

The mental health of employees has attracted an increasing degree of scientific attention. However, the majority of studies address the working population as a whole, with limited focus on young employees as a distinct group. The present narrative review synthesizes literature that was published in 2003-2025, which was identified through major online databases. This review focuses on young workers, most commonly defined in the literature as individuals aged 18-35 years who have entered the workforce within the past years. The aim of this review is to present the current state of knowledge with respect to the mental health of young workers, with particular attention to psychosocial risk factors contributing to this issue. The focus is on the differences between generations, the state of young employees' mental health, psychosocial work factors connected to it and identifies 6 key psychosocial risk factors associated with poorer mental health outcomes: poor psychosocial job quality, exposure to sexual harassment, emotional workload and work pressure, subjective job insecurity, effort-reward imbalance with low social support, and precarious employment. In the analyzed studies, young workers appear to be a group at risk of mental disorders, with work-related stressors playing a significant role in their occurrence. The review also compiles practical organizational solutions aimed at improving job quality, stability, support and minimizing psychosocial risks. Int J Occup Med Environ Health. 2026;39(2).

Intelligent Clinical Decision-Making Systems have become a cornerstone of modern healthcare by enabling accurate diagnosis, prognosis and treatment planning through data-driven insights. With the growing availability of heterogeneous healthcare data such as medical images, clinical records, physiological signals and textual reports, multimodal learning has emerged as a powerful paradigm for integrating diverse data sources. This review presents a comprehensive and systematic analysis of multimodal approaches for intelligent clinical decision-making by leveraging machine learning, deep learning, transfer learning and natural language processing techniques. A structured literature search was conducted using IEEE, Elsevier, Wiley Online Library and Springer databases, focusing on peer-reviewed studies published between 2020 and 2025. The selected articles were analysed based on data modalities, learning strategies, healthcare applications, datasets and performance evaluation metrics. This review highlights the effectiveness of multimodal frameworks in addressing key challenges such as class imbalance, disease prediction, patient monitoring and treatment planning. Additionally, it discusses the benefits, open challenges and limitations of existing intelligent clinical decision frameworks, including scalability, interpretability and real-world deployment issues. Finally, the review outlines future research directions emphasizing the integration of Internet of Things-enabled healthcare data, federated learning for privacy preservation and blockchain-based secure data sharing to enhance the reliability and clinical adoption of intelligent decision-making systems.

Serum uric acid has been linked to metabolic syndrome (MetS), although the relationship has been inconsistent. The uric acid-to-albumin ratio (UAR) has emerged as a novel composite biomarker that indicates the balance between systemic prooxidant burden and antioxidant capacity. This study aimed to evaluate the independent association between UAR and MetS in a large, real-world adult cohort. This cross-sectional study utilized retrospectively collected data from 4,596 adults (mean age: 38.78 ± 9.78 years; 64.6% female). MetS was defined according to the modified National Cholesterol Education Program - Adult Treatment Panel III (NCEP-ATP III) framework, excluding waist circumference due to significant missing data. Consequently, MetS classification was based on the remaining four criteria: triglycerides, HDL cholesterol, blood pressure, and fasting plasma glucose. UAR was calculated as uric acid (mg/dL) divided by albumin (g/L) and was dichotomized by the cohort median (low vs. high UAR). The prevalence of MetS was 7.0% (n = 320). Participants diagnosed with MetS were older and predominantly male compared to those without the condition (45.23 ± 9.93 vs. 38.29 ± 9.60 years; 61.2% vs. 33.4%, respectively; both p < 0.001). High UAR was more prevalent in the MetS group (70.9% vs. 44.6%, p < 0.001). Multivariable analysis revealed that age (OR 1.06, 95% CI 1.05-1.07), sex (female vs. male OR 0.43, 95% CI 0.33-0.56), and UAR group (OR 1.89, 95% CI 1.43-2.49) were independently associated with MetS (all p < 0.001). These findings indicate that UAR is significantly associated with MetS and adverse metabolic features, suggesting a relationship with cardiometabolic risk and reflecting an underlying metabolic imbalance.

Type VI secretion system effectors (T6SEs) are key virulence factors that disrupt critical cellular components in target cells, facilitating bacterial competition or host immune evasion. Accurate identification of T6SEs is therefore crucial for understanding bacterial pathogenesis. Although several computational predictors based on traditional machine learning and deep learning have been developed, their performance still requires improvement. In this study, we systematically evaluated a range of sequence-based features and embeddings from pretrained protein language models for T6SE prediction. Among these, ProtBert embeddings were identified as the most effective representation. Building on this finding, we present BERT-T6, a predictor fine-tuned from ProtBert via transfer learning for T6SE classification with imbalance awareness. It demonstrated good generalizability in independent test and achieved state-of-the-art performance, with a mean accuracy of 0.959, a sensitivity of 0.909, a specificity of 0.973, a precision of 0.905, an F1-score of 0.907, and an MCC of 0.881 in five repeated experiments. This work highlights the effectiveness of using a protein language model with transfer learning and imbalance-aware training for T6SE prediction. BERT-T6 provides a valuable tool for identifying T6SEs, supporting further investigation of bacterial virulence mechanisms.