Music is widely utilized to support recovery from stress, yet the precise neural mechanisms driving this therapeutic effect remain poorly understood. Crucially, it has been unclear whether music provides unique neurobiological benefits or simply acts as a general, relaxing auditory stimulus. Here, we demonstrate that music selectively facilitates multisystem recovery from acute stress-enhancing positive mood (PM) and accelerating autonomic stabilization (electrodermal activity and heart rate)-significantly outperforming an active natural sound control. Using functional MRI and dynamic causal modeling, we revealed the network architecture underlying this advantage. Compared to natural sounds, music drives a distinct reorganization of stress-regulation networks, establishing a directional, causal pathway from the auditory cortex to the insula and thalamus. On a faster timescale, dynamic functional connectivity reveals that this network reconfiguration is actively governed by musical structure: moment-to-moment fluctuations in perceived musical tension track transient shifts from stress-network engagement to neural downregulation. Furthermore, these music-specific connectivity changes are directly associated with improvements in PM. Together, these findings identify a temporally evolving mechanism by which the brain's processing of structured musical sequences acts as a dynamic regulatory driver, reconfiguring neural architectures to restore psychological and physiological homeostasis.

Bacterial contamination in liquid products remains a major challenge for food safety and public health, yet conventional detection methods generally rely on sampling-and-testing workflows that disrupt the original state of sealed containers. This limitation makes it difficult to perform nondestructive, item-level surveillance during transportation, storage, and consumer use. Here, we report a container-integrated plasmonic metasurface interface for camera-readable bacterial surveillance in liquid environments. The metasurface interface can be conformally integrated onto the inner surfaces of transparent containers through a clean nanotransfer lithography process, enabling long-term monitoring of contamination-related changes. Instead of relying on spectral interrogation, bacterial attachment and proliferation processes are converted into spatial transmission image patterns that can be directly acquired using a camera or smartphone. This image-based readout enables in situ monitoring of bacterial proliferation and quantitative detection of Escherichia coli in closed liquid systems. Furthermore, deep image-feature analysis enables label-free grouping of E. coli, Staphylococcus aureus, and their mixed samples with an accuracy of 95%. This work establishes a container-integrated metasurface interface for sampling-free bacterial surveillance, offering a promising route toward nondestructive microbial screening in beverage safety and other closed liquid systems.

Rapid changes in temperature and precipitation present a major challenge for long-lived forest trees, as their long generation times limit the required rate of adaptive responses within populations. Common garden experiments provide valuable insights into local adaptation, but belowground responses remain understudied. We investigated fine root metabolomes, biochemistry, and structure in 54-year-old Pinus sylvestris originating from two contrasting environments: a local lowland site and a colder high-elevation mountain region, both grown in a common garden. Untargeted GC-MS/MS profiling identified 160 metabolites and revealed pronounced effects of both season and tree origin. In spring, roots from high-elevation trees exhibited a distinct metabolomic profile characterized by lower concentrations of nitrogen-containing compounds and elevated levels of lipid derivatives, including fatty acids and sterols. In contrast, autumn roots from both origins showed broadly similar metabolite abundances. According to biochemical analyses, seasonal shifts were also evident in carbohydrate dynamics, with higher concentrations of non-structural carbohydrates in spring and increased structural carbohydrates deposition in autumn. Nitrogen compounds, particularly amino acids, increased in autumn roots of both tree origins, most likely reflecting nutrient storage. Differences related to tree origin extended to root morphology, as roots from high-elevation trees had higher cell wall carbohydrate content and altered root branching patterns compared to those from lowland trees. Our findings suggest that tree origin leaves a clear imprint on the fine root metabolome and structure of mature Scots pine, with most pronounced differences occurring in early spring. These results highlight how inherited adaptation to origin climates interacts with seasonal soil processes. High-resolution metabolomics thus offers a powerful tool for detecting subtle belowground provenance-related belowground effects that may be overlooked by traditional trait-based approaches, providing new perspectives on possible tree adaptation under climate change.

Ciprofloxacin resistance in Salmonella Typhimurium is a significant public health concern, and the mechanisms by which the resistance evolves are poorly defined. Here, by serial passaging under antibiotic selection, we isolated ciprofloxacin-resistant S. Typhimurium mutants and subjected them to whole-genome sequencing to reveal the major mutations associated with resistance. The Low Cip[R] mutant acquired four chromosomal mutations in ramR, icdA, lipB, and gyrA, and the High Cip[R] mutant gained additional mutations in gyrB, yaiC, and corA. Functional characterization determined that mutations in ramR resulted in efflux pump upregulation, while disruptions in the TCA cycle caused by mutations in icdA and lipB led to metabolic alterations. These changes indirectly enhanced resistance by increasing the expression of the global regulator MarA and reducing OmpF-dependent membrane permeability. Despite the observation of the G105A substitution in GyrA, enzymatic assays confirmed the failure to support resistance to ciprofloxacin, possibly because the structural alteration remained minimal. GyrB[488-489dup] was associated with maintained supercoiling under ciprofloxacin and enhanced fluoroquinolone resistance, suggesting a major role in resistance evolution. Other mutations in yaiC impaired biofilm and, in corA, intracellular accumulation of magnesium, possibly stabilizing the bacterial cell envelope under antibiotic pressure. The findings provide novel explanations for the multifaceted mechanisms leading to ciprofloxacin resistance in Salmonella and suggest targets to combat antimicrobial resistance.IMPORTANCEAntibiotic resistance in Salmonella Typhimurium is an increasing public health concern, yet the genetic changes that allow bacteria to become resistant are not fully understood. In this study, we evolved ciprofloxacin-resistant Salmonella in the laboratory and identified the mutations that arise during resistance development. We found that resistance does not result from a single change but from multiple adaptations affecting drug efflux, metabolism, and the antibiotic target. Some mutations increased the activity of pumps that remove antibiotics from the cell, while others altered bacterial metabolism and reduced membrane permeability, making it harder for the drug to enter. A duplication in the DNA gyrase subunit GyrB played a particularly important role in maintaining DNA function under antibiotic stress. Together, these results reveal how diverse genetic changes cooperate to generate ciprofloxacin resistance and provide insights that may help guide strategies to combat drug-resistant Salmonella infections.

Coordinated changes in gene expression, epigenetic regulation, protein and metabolic activities together drive disease progression and determine clinical outcomes. While spatially resolved transcriptomics has been widely adopted across biomedical fields, it offers an incomplete picture limited to transcriptomic levels. Here, we survey the latest developments in spatial multiomics technologies, with particular emphasis on platforms that extend beyond conventional transcriptomics and profile genomics, epigenomics, proteomics, or metabolomics within intact tissues. These approaches are rapidly becoming commercialized, and here we highlight major technical breakthroughs, enhanced sample compatibility, emerging applications, and computational tools for data analysis. This Review aims to equip researchers with a clear understanding of the current technological landscape and to accelerate the adoption of spatial multiomics methods in biomedical research.

Severe trauma remains a major global cause of death and disability, particularly among individuals younger than 45 years. Emergency departments are the main gateway to trauma care, where rapid risk stratification guides resuscitation, surgical decision-making, intensive care admission, and resource allocation. Conventional tools, including the Revised Trauma Score, Glasgow Coma Scale, Injury Severity Score, and Trauma and Injury Severity Score, standardize early assessment but rely largely on static variables and may not capture dynamic physiological changes during resuscitation. Dynamic biomarkers offer additional biological information. Glial fibrillary acidic protein, S100B, neuron-specific enolase, serum lactate, and lactate clearance may reflect neuronal injury, tissue hypoperfusion, metabolic stress, and how effectively resuscitation is restoring perfusion. Artificial intelligence and machine-learning models can integrate vital signs, laboratory findings, imaging data, biomarkers, and demographic variables to generate individualized predictions of mortality, clinical deterioration, intensive care requirements, and complications. Recent studies suggest that some machine-learning models may outperform conventional scoring systems; however, limitations involving data heterogeneity, algorithmic bias, interpretability, external validation, privacy, and clinical workflow integration remain. This narrative review synthesizes current evidence on conventional trauma scoring systems, dynamic biomarkers, and artificial intelligence-based prediction models for early risk stratification of severe trauma in emergency departments. It also examines the potential of multimodal frameworks that combine physiological, anatomical, molecular, and computational information. Although integrated prediction systems may improve accuracy and clinical decision-making, prospective multicenter validation and evidence of real-world clinical benefit are required before routine implementation.

Managing acute illness in older populations remains a major challenge because of the complex interplay between multimorbidity and reduced physiological reserve. While conventional acute severity scores (e.g., Sequential Organ Failure Assessment, Acute Physiology and Chronic Health Evaluation, and National Early Warning Score) track acute physiological derangements, they often fail to capture the biological heterogeneity seen in geriatric patients. Frailty provides a vital window into the baseline vulnerability of a patient, yet it remains underutilized in acute triage despite its established prognostic value. Current evidence demonstrates that integrating frailty measures, specifically the Clinical Frailty Scale (CFS), with existing severity scores significantly improves mortality prediction and risk stratification in emergency and critical care. This mini-review explores age-related physiological changes as well as the prognostic limitations of standard severity scores while evaluating their integration with validated frailty tools. To bridge current clinical gaps, we propose a practical 4-step clinical triage algorithm integrating a baseline "Two-Week Rule" CFS with acute severity scores via a synergistic 2 × 2 risk stratification matrix. By identifying distinct clinical phenotypes, this framework guides precise, person-centered triage, ensuring that treatment escalation and critical care interventions remain medically proportionate to an older adult's attainable functional recovery.

Although the mitochondria are known as the cellular powerhouse, their function is beyond energy generation. These organelles regulate cellular metabolism, yet maintains a tightly regulated reactive oxygen species (ROS) generation and optimal redox state. In addition, mitochondria serve as mediators of physiological and pathological processes, such as maintenance of calcium balance, and control of apoptosis and mitophagy. All these make the mitochondria a major factor in both cellular and organismal regulation. However, mitochondria dysfunction may occur through many processes, including genetic mutations, increased production of ROS, metabolic failure from impaired electron transport chain activity, and dysregulated dynamics or mitophagy. Several self-perpetuating damages accumulate from these processes and influence clinical pathologies, such as aging, metabolic syndrome, cancer, neurodegeneration, and reproductive disorders. Recent studies demonstrate promising therapeutic targets for mitochondrial dysfunction. Examples include targeted antioxidants, such as MitoQ and SkQ1, to selectively neutralize mitochondrial ROS, pharmacological modulators to enhance mitochondrial biogenesis and to restore NAD[+] homeostasis via PGC-1α activation, gene-editing technologies, such as mitoTALENs and mtZFNs to selectively eliminate pathogenic mitochondrial DNA mutations, and mitochondrial transplantation as a new technique to replace damaged organelles. Together, these novel approaches highlight the need for research in mitochondrial function to change the therapeutic landscape in the management of mitochondrial dysfunction-associated diseases.

Airway epithelial injury is a central feature of asthma and contributes to persistent airway inflammation, mucus overproduction, and airway remodeling. House dust mite (HDM) is a major aeroallergen associated with allergic asthma, yet the intracellular stress responses underlying HDM-induced epithelial damage remain incompletely understood. Here, we investigated changes in the autophagic process and apoptotic signaling during HDM-induced airway injury using both in vivo and in vitro models.