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PubMed2026

ER proteostasis failure in HYOU1 deficiency alters B cells, neutrophils, and interferon signalling.

Hypoxia upregulated 1 (HYOU1) is a stress-inducible ER chaperone. We investigated 2 unrelated patients carrying biallelic HYOU1 variants and presenting with primary immunodeficiency. Patient 1, homozygous for p.Pro444His, displayed failure to thrive, hypoglycemia, B cell lymphopenia, and neutropenia. Patient 2, compound heterozygous for p.Arg262Gln and p.Pro757_Glu758insAla, exhibited recurrent infections, enteropathy, and hypogammaglobulinemia. In Patient 1, while HYOU1 transcription was preserved, the protein was severely reduced. Tunicamycin treatment of dermal fibroblasts showed a blunted unfolded protein response and defective induction of ER stress-responsive genes. Immunophenotyping showed near-absence of circulating B cells, and single-cell RNA sequencing of bone marrow identified an arrest at the pro-B cell stage. Neutrophils displayed hypogranulation and dysregulated IFN- and apoptosis-associated transcriptional signatures, unresponsive to G-CSF. HYOU1 deficiency hence results in ER stress-induced proteostasis failure that simultaneously impairs adaptive immunity through B cell developmental arrest and innate immunity through neutrophil dysfunction and IFN pathway imbalance. This work expands the spectrum of HYOU1 deficiency and further identifies ER proteostasis as a central determinant of immune homeostasis.

Journal Article↗

PubMed2026

Liver transcriptome analysis revealed multiple immune processes and lipid metabolism pathways involved in the defense response of the turbot (Scophthalmus maximus) against Aeromonas salmonicida.

Aeromonas salmonicida is a significant pathogen causing notable economic losses in Scophthalmus maximus aquaculture. This study utilized Illumina sequencing technology to examine the transcriptional response characteristics of S. maximus liver at 24 h following A. salmonicida infection. A total of 2363 differentially expressed genes (DEGs) were identified when compared to the negative control group. The immunity-related Toll-like receptor signaling pathway, NOD-like receptor signaling pathway, as well as metabolism-related PPAR signaling pathway and insulin signaling pathway, were notably enriched. Significant differences exist in the expression of key genes within the PPAR pathway, particularly cd36, acsl4a, pparαa, and plin2, all of which mediate the interaction between lipid metabolism and the immune response. These results offer valuable insights into the immunometabolic regulatory mechanism of S. maximus response to A. salmonicida infection.

Animals↗

PubMed2026

Understanding specificity in immune-brain pathways: A systematic review of differential associations between individual cytokines and brain structure and function measured through magnetic resonance imaging in humans.

Research shows that cytokines are associated with psychiatric disorders, including major depression, and multiple aspects of brain structure and function. Accumulating data suggest that different cytokines may have unique profiles of biological activity, particularly in their neuromodulatory roles, but it is currently unclear whether they have unique associations with specific neural circuits in humans. In this paper, we systematically review magnetic resonance imaging studies conducted with depressed or healthy control human participants under age 65 that examine associations between peripheral cytokines and brain structure and function, with the goal of evaluating evidence for the specificity of these cytokine-brain associations. We find that across multiple measures of brain structure and function, the majority of studies reviewed reported unique associations between individual cytokines and brain outcomes. A synthesis of findings across studies also suggests a preliminary hypothesis of specific associations of interleukin-6 levels in circulation with the default mode network and tumor necrosis factor-alpha with the salience network, which could be tested in future research. We conclude the review with future directions for research that can strengthen understanding of these associations.

Humans↗

PubMed2026

Comparative Transcriptomics Reveals Shared Downstream Pathways in Craniofacial Pathology.

Treacher Collins syndrome and Nager syndrome are craniofacial developmental disorders caused by defects in ribosome biogenesis and RNA splicing, respectively, yet they exhibit overlapping abnormalities affecting neural crest cell-derived craniofacial structures. To investigate shared downstream pathogenic mechanisms, we performed a comparative transcriptomic analysis of zebrafish polr1c and sf3b4 mutant models from our previous studies. Comparative analysis identified 17 shared differentially expressed genes (DEGs) between polr1c and sf3b4 mutants, with the majority of shared genes dysregulated in the same direction, indicating a coordinated rather than random transcriptional response. Gene ontology analysis identified ATP-dependent protein folding chaperone activity as the only shared molecular function, driven in part by upregulation of hsp90aa1.2, indicating a common proteostasis response. Because chaperone activity is linked to extracellular matrix (ECM) protein processing, we cross-referenced DEGs from both mutants against the curated zebrafish matrisome. Three of the 17 shared DEGs (serpinh1b, il11a, and lepa) were matrisome-associated and upregulated in both mutants. Serpinh1b, a collagen-specific chaperone, was strongly expressed in craniofacial cartilage and mesenchymal populations during pharyngeal arch development and exhibited nearly identical fold changes in both mutants. Il11a is of particular interest because its receptor, IL11RA, is known to be associated with human craniosynostosis, suggesting potential relevance to craniofacial development. Together, it is possible to hypothesize that shared chaperone-associated transcriptional changes, together with altered ECM-related gene expression, may contribute to polr1c- and sf3b4-associated craniofacial disorders, warranting further functional validation.

Extracellular Matrix↗

PubMed2026

Accurate quantification of canine mitochondrial DNA copy number from canine blood and brain samples.

Acute brain injury is difficult to evaluate in veterinary medicine and tools to investigate the potential involvement of mitochondrial involvement are limited. The brain is highly enriched in mitochondria and contains thousands of copies of mitochondrial DNA (mtDNA) per cell, but robust methods for quantifying mitochondrial DNA copy number (mtDNA-CN) in canine tissues are lacking. We describe the development of a quantitative real-time PCR assay for absolute measurement of mtDNA-CN which was validated in canine blood and brain tissue. To minimize amplification of nuclear mitochondrial insertion sequences (NumtS) and repetitive regions, species-specific oligonucleotide primers were designed following in silico genomic filtering. The assay was applied to a small pilot cohort comprising blood samples from dogs with and without acute brain injury (n = 4-6 per group) and cerebral cortex samples (n = 1 per group) to assess feasibility and biological plausibility. In non-brain injury dogs, blood mtDNA-CN ranged from 98 to 288 copies per nuclear genome (mean 193 ± 72), while values in brain-injured cases ranged from 163 to 228 copies per genome (mean 200 ± 33). Cerebral cortex samples exhibited higher mtDNA-CN than blood, consistent with known tissue-specific mitochondrial enrichment. In a single brain-injured case with serial sampling, mtDNA-CN increased over five days. This study presents a validated assay and pilot data for mtDNA-CN quantification in canine samples. While not powered for biomarker evaluation, this method may enable future studies of mitochondrial dynamics in canine brain injury and metabolic disease.

Animals↗

PubMed2026

The role of artificial intelligence in the diagnosis and prognosis of traumatic brain injury based on brain CT scans: a systematic review.

Traumatic brain injury (TBI) is a leading cause of emergency department visits and a major contributor to injury-related mortality and long-term neurological disability. Non-contrast computed tomography (CT) is the gold-standard imaging modality for the rapid diagnosis of TBI. Clinical outcomes depend strongly on early detection and prompt acute management. Artificial intelligence (AI)-based models may support faster automated identification of traumatic findings and early prediction of patient prognosis. A systematic literature search was conducted in PubMed/MEDLINE, Scopus, IEEE Xplore, ACM Digital Library, and the Cochrane Library in accordance with PRISMA 2020 guidelines to evaluate AI-based models for automated detection of TBI-related findings on CT and for prediction of clinical outcomes. Risk of bias and applicability were assessed using QUADAS-2 for diagnostic accuracy studies and PROBAST + AI for prediction model studies. Twenty-two studies were included. Sixteen studies evaluated diagnostic tasks and 10 evaluated prognostic outcomes, with four studies contributing to both categories. Diagnostic performance was generally high, with many studies reporting AUC values approaching or exceeding 0.90, particularly for larger lesion volumes.Prognostic performance was more variable, with moderate to high discrimination and substantial heterogeneity. Only 9 studies incorporated independent external validation, and performance was frequently lower in external cohorts. All prognostic model studies were judged to be at high overall risk of bias using PROBAST + AI, and most diagnostic accuracy studies also demonstrated high or unclear risk of bias in at least one QUADAS-2 domain, most frequently in patient selection. AI-based models applied to brain CT demonstrate strong technical performance for both diagnostic and prognostic tasks in TBI. However, most studies relied on retrospective designs and lacked independent external validation which limits models generalizability and raises concern for potential overfitting. Prospective, multicenter studies with standardized methodologies and rigorous external validation are required before widespread clinical implementation.

Humans↗

PubMed2026

A systematic review of macaque brain stimulation: Trends and future directions.

Neurostimulation techniques can powerfully modulate neural circuit activity and provide causal insights into the relationship between brain function and behavior. Macaque monkeys have long been a key animal model for brain stimulation studies. While stimulating the macaque brain with one or a few electrodes has already taught us much about brain function and dysfunction, recent technological advances promise a future with more precise stimulation using many more electrodes. However, such possibilities also increase the number of choices an experimenter has when designing their study. We can learn from a rich past, but a comprehensive overview of which brain regions have been studied and with what stimulation parameters is lacking. Here, we present a PRISMA-compliant systematic review of 734 macaque brain stimulation studies using electrical and/or optogenetic stimulation. We find a striking bias in which brain areas have traditionally been stimulated: a mere 10 brain regions account for half of all studies, with the remainder of studies investigating approximately 150 other areas. Across studies, stimulation frequency robustly predicted direct behavioral effects independent of brain region, while amplitude did not. Future studies could more systematically explore less studied regions through lower stimulation frequencies (e.g., 20-50 Hz) alongside established ranges (∼200 Hz). Tools such as fMRI or optical imaging can capture neural circuit engagement evoked by these frequencies, even when behavioral effects are absent or remain subtle. Our synthesis offers a guide towards the next steps in high-channel-count, high-precision stimulation approaches.

Animals↗

PubMed2026

Somatic mutations: recent advances in brain aging and neurodegeneration.

Somatic mutations are genetic variants that occur after the single-cell phase of development and have been implicated in disease pathogenesis. While most DNA lesions are detected and repaired, examination of healthy tissue has revealed that some lesions escape repair, leading to somatic mutations that accumulate at a consistent rate, including in human brain tissue and postmitotic neurons. Emerging methodological and analytical advances have revealed the presence of persistent mutagenic mechanisms during healthy brain aging as well as mutational pattern shifts in the context of neurodegenerative diseases. Here, we highlight recent methodological advances, summarize our current understanding of somatic mutagenesis in neurotypical brain aging, and examine the role of somatic mutations in neurodegenerative diseases.

Humans↗

PubMed2026

Origins and timing of somatic variants in the brain.

Somatic variants accumulate in human brain cells throughout the lifespan. Variant allele fraction has traditionally been used as a proxy for both the developmental timing of somatic variants and their functional effect, based on the assumption that earlier mutations are shared by larger cell populations and therefore have greater potential for severe phenotypes. However, recent discoveries challenge this simplified model. Variables such as developmental bottlenecks, lineage restriction, and cellular and molecular context play critical roles in shaping the distribution and functional impact of somatic variants in the brain. These insights support a shift toward a context-dependent framework for interpreting somatic mosaicism.

Humans↗

PubMed2026

Strategies for mosaic variant calling in brain disorders.

The human brain is a genomic mosaic, where postzygotic mutations arising from embryogenesis to senescence drive diverse neurodevelopmental and neurodegenerative diseases. Because of numerous sequencing artifacts at ultralow variant allele frequencies (VAFs), detecting these variants remains a significant analytical challenge. This review focuses on single-nucleotide variants and small indels, summarizing current strategies for aligning sampling methods, including bulk, laser capture microdissection, and single-cell genomics, with the expected clonal architecture of the brain. It emphasizes that mosaic detection sensitivity is fundamentally constrained by sequencing depth, since even the most advanced algorithms cannot identify variants not physically represented in the sequencing library. The review further recommends the selection of variant calling algorithms based on validated VAF detection performance, matching tools like MuTect2 and MosaicForecast to their optimal performance ranges. Furthermore, we discuss how multitissue sampling, as emphasized by the SMaHT project, addresses the matched-control dilemma and supports accurate variant classification via cross-tissue VAF gradients. Integrating these established pipelines with multiomics modalities, including transcriptomic and epigenetic data, could advance the field toward a functional understanding of how the somatic genome impacts human brain health and disease.

Humans↗

PubMed2026

Analysis of the Relationship between Early Clinical Factors and Glasgow Outcome Scale in Patients With Traumatic Brain Injury.

OBJECTIVE: This study aimed to evaluate the association between early clinical factors and the Glasgow outcome scale (GOS) in patients with traumatic brain injury (TBI). METHODS: We conducted a retrospective analysis of 98 TBI patients who underwent emergency surgery between January 2021 and January 2024. Based on GOS scores at 6 months post-surgery, patients were classified into a favorable outcome group (GOS ≥ 4, defined as moderate disability or good recovery, n = 58) and an unfavorable outcome group (GOS < 4, i.e., death, persistent vegetative state, or severe disability, n = 40). Baseline and early clinical parameters were compared between groups. Statistically significant variables from univariate analysis were entered into a multivariate logistic regression model to identify independent prognostic factors. RESULTS: Significant intergroup differences were observed in age, time from injury to surgery, bleeding site, midline shift, Glasgow coma scale (GCS) score at admission, blood glucose level, and D-dimer level (all p < 0.05). Multivariate analysis confirmed that age, time from injury to surgery, GCS score, blood glucose, and D-dimer level were independent predictors of GOS (all p < 0.05). CONCLUSION: Early clinical factors, including age, time to surgery, GCS score, blood glucose, and D-dimer level, independently influence GOS in TBI patients. Time from injury to surgery emerged as a potentially modifiable factor in this cohort, suggesting that minimizing delays may improve outcomes.

Humans↗

PubMed2026

Epigenetic drift and LINE-1 activation in aging brain: Implications for neurodegenerative disease.

Brain aging and age-associated neurological diseases, such as Alzheimer's Disease (AD), Parkinson's Disease (PD), and Amyotrophic Lateral Sclerosis (ALS), are largely attributed to epigenetic drift which is characterized by the gradual accumulation of alterations in neural cell methylation patterns over time. These methylation changes are particularly evident in transposable element (TE)-derived sequences such as Long interspersed element-1 (LINE-1) which comprises approximately 17% of the human genome. During aging, LINE-1 elements gradually lose their methylation, as well as the regulatory safeguard mechanisms that usually keep them inactive. This repression loss can lead to LINE-1 reactivation, contributing to harmful effects including genomic instability, neuroinflammation, and more. Together these findings indicate that impaired epigenetic maintenance, especially in repetitive genome regions, plays a key role in biological aging of neurons and glial cells. In this narrative review, we discuss the methylation dynamics and regulatory mechanisms of LINE-1 retrotransposons, their activation processes during aging, and contribution to age-associated neurological diseases. We also highlight the potential of targeting LINE-1 methylation to restore methylation homeostasis, epigenetic stability and delay brain aging.

Humans↗

PubMed2026

The interplay between circadian misalignment or sleep disturbances and cognition and brain function in individuals with different degrees of insulin resistance - a systematic review.

Disruption of sleep increases the risk of type 2 diabetes and worsens cognitive outcomes, yet few studies have evaluated the interaction between insulin resistance and sleep parameters in relation to cognitive outcomes or the risk of dementia. This systematic review examines how circadian misalignment and sleep disturbances affect cognition and neuroimaging findings in individuals with varying degrees of insulin resistance. Across 27 studies, disrupted circadian rhythmicity and sleep disturbances were negatively associated with brain health, possibly through its effects on insulin sensitivity, whereas the impact of sleep duration and quality were inconclusive. Methodological heterogeneity, reliance on cross-sectional designs, and limited control for confounders restricted definitive conclusions and highlighted the need for longitudinal and interventional studies with objective measurements. Nonetheless, the findings support circadian rhythmicity as a potentially modifiable risk factor for preserving cognition in insulin-resistant populations. Future research should prioritise prospective and interventional studies and focus on biological markers rather than self-reported outcomes.

Humans↗

PubMed2026

Applications of quantum AI in brain disorder diagnosis: A systematic review.

BACKGROUND AND OBJECTIVE: Brain disorder diagnosis and prediction remain challenging because neuroimaging, electrophysiological, behavioral, and multimodal data are high-dimensional, noisy, heterogeneous, and limited by small clinical cohorts. This systematic review synthesised applications of quantum artificial intelligence (QAI) for brain disorder diagnosis, prediction, detection, and monitoring. METHODS: Following PRISMA guidelines, studies published from 2016 to 13 January 2026 were retrieved from Scopus, Web of Science, and IEEE Xplore. After screening, 36 studies met the eligibility criteria and were qualitatively analysed according to disorder category, data modality, QAI method, implementation setting, validation strategy, and performance. RESULTS: At the broader disease-group level, neurodegenerative disorders were the most frequently investigated, followed by mental health and psychiatric disorders. At the individual level, Parkinson's disease and schizophrenia were the leading applications, followed by depression, anxiety, Alzheimer's disease, and stress-related tasks. MRI-based modalities were the most frequently used data source, followed by multimodal data and EEG. Methodologically, primary QAI approaches were dominated by quantum neural and QDL architectures, followed by quantum-inspired optimization or feature-selection methods and quantum-kernel/conventional QML classifiers. Qiskit/IBM Quantum and PennyLane were the most frequently reported quantum software frameworks. However, most studies relied on simulators, classical quantum-inspired implementations, or unclear implementation settings, with limited real-hardware evaluation. CONCLUSIONS: QAI shows emerging potential for brain disorder analysis, particularly through hybrid quantum-classical learning, quantum neural architectures, quantum-kernel methods, and quantum-inspired optimization. Nevertheless, current evidence remains preliminary and requires larger datasets, subject-level and external validation, fair classical benchmarking, noise-resilient circuits, real quantum hardware evaluation, explainability, and clinical validation.

Humans↗

PubMed2026

Integrated miRNA-mRNA profiling reveals candidate regulatory relationships associated with high-fat diet-induced muscle lipid deposition in black seabream (Acanthopagrus schlegelii).

High-fat diets are increasingly used in aquaculture due to their protein-sparing effects; however, the post-transcriptional regulatory mechanisms of fish muscle in response to high-fat diets (HFD) remain unclear. In this study, juvenile black seabream were fed either a normal-fat diet (NFD) or a HFD to investigate the miRNA-mRNA regulatory network associated with diet-induced muscle lipid deposition. Oil Red O staining and biochemical analysis showed that high-fat diet feeding markedly increased lipid droplet accumulation and crude lipid content in muscle, indicating significant induction of muscle lipid deposition. Integrated mRNA and miRNA expression profiling revealed substantial transcriptomic and post-transcriptional responses to high-fat diet challenge. A total of 271 differentially expressed genes were identified, including 120 upregulated and 151 downregulated genes. Through combined target prediction and expression correlation analysis, thirteen candidate inverse miRNA-mRNA relationships were subsequently identified, and RT-qPCR supported the expression patterns of selected miRNAs and mRNAs. These pairs included miR-499-x-dmgdh, miR-499-y-gatm, miR-727-y-ass1, miR-4649-x-foxo4, miR-9129-z-myl7, and several novel miRNA-mediated interactions involving adk, chst11, lypla2, frem2, kcnc4, wars1, bag2, and capn2. Functional analysis suggested that these regulatory pairs were mainly associated with metabolic adaptation, structural remodeling, and cellular stress responses. In particular, gatm, dmgdh, ass1, and adk were associated with energy metabolism-related processes, including pathways previously linked to Ampk regulation, whereas myl7, frem2, and kcnc4 may contribute to muscle structural maintenance and excitability regulation. Overall, this study provides candidate miRNA-mRNA regulatory relationships potentially involved in high-fat diet-induced muscle lipid deposition and adaptive remodeling in black seabream, offering a basis for future functional studies on muscle metabolism and quality regulation in marine fish.

Animals↗

PubMed2026

Quality assessment, prognostic factors, and biomarkers for brain tumor analysis: a comprehensive systematic review.

The brain tumors possess different causative factors and properties, making their diagnosis and treatment difficult. Growth of these cancers usually leads to compression of the adjacent nerves and obstruction of the flow of cerebrospinal fluid, thus leading to increase in intracranial pressure. This affects the working of brain in many ways; thus, the difficulty involved in its treatment. With the improvements in technology in neuroimaging, including Diffusion Tensor Imaging (DTI), Positron Emission Tomography (PET), and multiparametric Magnetic Resonance Imaging (mpMRI), the diagnosis process has become easy. The effectiveness of any form of therapy in such patients depends primarily on their prognosis. While it is a common practice that physicians determine the prognosis of the disease by considering the age of the patient, histological grade of the tumor, and resection status, now this method has become more comprehensive by adding molecular signature and genetic analyses to the list of criteria. Next-generation sequencing (NGS) allows a reliable molecular classification. It increases the level of risk stratification, facilitating the application of therapies tailored to individual patients. Thus, molecular oncology has greatly changed our views on brain tumors' pathology and prognosis while neoadjuvant treatments aim at increasing the survival rate. On the other hand, radiogenomics is a field of study that combines non-invasive imaging phenotypes and genomic information in order to find unique molecular signatures of tumors without collecting samples from tumors. Molecular biomarkers are absolutely essential in the diagnosis of cancer, treatment monitoring, and recurrence of cancer. Advances in liquid biopsy technology, particularly the methods for circulating tumor DNA (ctDNA) and Extracellular Vesicle (EV) based analysis, have enabled the possibility of non-invasive monitoring of the progression of the tumors over time. This review highlights key studies and important scientific works about imaging technologies, biomarkers, and prognostic factors of malignant brain tumors.

Humans↗

PubMed2026

Brain network alterations underlying cue reactivity and craving in abstinent methamphetamine users: a systematic review of functional MRI findings.

BACKGROUND: Methamphetamine use disorder (MUD) is marked by intense craving and high relapse risk, often triggered by drug-related cues. Functional magnetic resonance imaging (fMRI) provides key insight into the neural basis of this cue reactivity, implicating large-scale brain networks for reward, motivation, and control. Yet, findings remain inconsistent across studies due to differences in task design, abstinence duration, and participant characteristics. OBJECTIVE: This systematic review synthesises evidence on how abstinence influences brain network alterations underlying cue reactivity and craving in methamphetamine users, integrating task-based and resting-state fMRI findings within leading neurobiological models of addiction. METHODS: A systematic search of PubMed, Scopus, Web of Science, and Ovid was conducted up to August 10, 2025, following PRISMA 2020 guidelines. Eligible fMRI studies examined cue reactivity or craving in abstinent methamphetamine users. Data were extracted on activation, connectivity, and brain-behaviour associations, and synthesised narratively. RESULTS: Task-based studies revealed heightened activation across reward, salience, and control networks during cue exposure, which diminished as parietal and executive control systems re-engaged with longer abstinence. Resting-state findings showed disrupted intrinsic connectivity among default mode, salience, and frontoparietal networks, reflecting persistent imbalances linked to craving and use severity. CONCLUSION: fMRI evidence shows that MUD is marked by network-level disruption linking reward, salience, and control systems. Task-based findings reveal strong cue reactivity in reward circuits, while resting-state data show persistent imbalance among default mode and control networks. With abstinence, partial restoration of network integrity emerges, highlighting both vulnerability and opportunities for targeted, recovery-based interventions.

Humans↗

PubMed2026

Effects of strength and balance training on the structure of the aging brain.

BACKGROUND: While it is established that motor training induces structural changes in the brains of young adults, structural adaptations in aging brains are less studied. METHODS: This randomized controlled study investigated the impact of long-term strength and balance training on the structural plasticity in 60 elderly adults (64 - 82 years old, 70.6 ± 4.7) using multi-modal neuroimaging. We compared the effects of three months of strength training to balance training of the same duration and to a passive control group. Voxel-based morphometry (VBM) and tract-based spatial statistics (TBSS) were used to assess grey matter (GM) and white matter (WM) plasticity. White matter tract integrity (WMTI) modelling was employed to explore the microstructural underpinnings of white matter alterations. RESULTS: We found that strength training was associated with changes in diffusion metrics consistent with white matter microstructural remodeling, specifically increased extra-axonal axial diffusivity in the bilateral inferior fronto-occipital and longitudinal fasciculi. Additionally, both balance and strength training mitigated reductions in axonal water fraction in the splenium of the corpus callosum and the right posterior corona radiata observed in the control group. CONCLUSION: These results underscore the potential relevance of strength and balance training to induce beneficial neural plasticity by counteracting aging-related demyelination in the corpus callosum and highlight the specific role of strength training in facilitating white matter reorganization in key transmission fiber pathways.

Humans↗