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PubMed2026

Machine learning-assisted Mn-N-C nanozyme colorimetric sensor array for trace-level detection of biogenic amines in meat.

Accurate detection of biogenic amines (BAs) in meat remains challenging due to their high structural similarity and co-occurrence. Herein, an Mn-N-C nanozyme was synthesized via a metal-organic framework confined pyrolysis strategy, possessing excellent oxidase (OXD)- and peroxidase (POD)-like activities. The dual enzyme-like activity showed Km values of 0.1584 mM (OXD) and 0.1498 mM (POD), respectively, in detection system. Leveraging these properties, a colorimetric sensor array was constructed, enabling the detection of four representative BAs within a concentration range of 2-10 ppm with 100% classification accuracy. In addition, a concentration independent recognition model based on an artificial neural network was developed to address signal nonlinearity interference in meat. The integrated system achieved accurate trace-level identification of BAs in perishable fish, pork, and chicken, demonstrating its applicability for early-stage BAs monitoring and quality deterioration warning during storage and transportation.

Biogenic Amines↗
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

Microglial modulation in general anesthesia: molecular.

General anesthetics profoundly alter brain function and consciousness, yet the mechanisms underlying these effects remain incompletely understood. Although traditional studies have primarily focused on neuronal targets, accumulating evidence suggests that microglia dynamically respond to anesthetic exposure and may participate in anesthesia-associated neurophysiological changes. Beyond their established immune functions, microglia are increasingly implicated in synaptic remodeling, metabolic regulation, neuronal activity surveillance, and neuron-glia communication. Recent studies indicate that different classes of anesthetic agents modulate microglial activity through diverse and context-dependent mechanisms involving inflammatory signaling, purinergic pathways, calcium dynamics, mitochondrial metabolism, and neural circuit interactions. These responses are associated with postoperative neurocognitive disorders, altered synaptic plasticity, and anesthesia-related changes in brain states. In this review, we summarize current evidence regarding the effects of volatile anesthetics, intravenous anesthetics, and analgesics on microglial function and discuss the molecular, functional, and circuit-level mechanisms underlying anesthesia-associated neuron-microglia interactions. We further highlight the dynamic and heterogeneous nature of microglial responses during anesthesia and discuss current limitations in the field, including the lack of temporally resolved and cell-specific approaches. Understanding these processes may provide insights into anesthesia-associated neurocognitive dysfunction and support the development of neuroimmune-targeted strategies in anesthesiology.

General anesthesia↗
PubMed2026

Ensemble DNA methylation clock demonstrates Immune-metabolic aging signatures associated with mortality.

Aging is a multifactorial process that is best described in terms of the progressive acquisition of multiple layers of phenotypic changes, such as epigenetic modifications, inflammation, and metabolic dysregulation. DNA methylation clocks have been extensively used to construct epigenetic clocks based on the DNAm profiles that can be used to estimate biological age and predict age-associated outcomes. Nevertheless, the vast majority of clocks constructed so far have been based on linear models, which are unlikely to fully account for the heterogeneity and non-linearity of survival-related DNAm signatures. In this work, we constructed a heterogeneous stacked ensemble survival model based on DNAm data obtained from the Framingham Heart Study. We first identified 190 CpG loci using elastic net Cox regression and subsequently constructed a survival prediction model based on the fusion of five complementary survival models by means of a neural network meta-learner. The prediction power of the survival model was evaluated in an external validation cohort, where we observed strong performance for predicting all-cause mortality that significantly exceeded PhenoAge and was statistically comparable to GrimAge. These performance estimates were derived in cohorts of European ancestry and externally validated in postmenopausal women aged 50-79 years, and should therefore be interpreted as applicable only to demographically similar populations.

Humans↗
PubMed2026

The Thyroid-Brain Network: Exploring Inflammation, Immune Mechanisms and Common Triggers in Thyroid-Related Neurological Dysfunction.

Autoimmune thyroid diseases (AITD), including Hashimoto's thyroiditis and Graves' disease, represent the most prevalent endocrine disorders worldwide, affecting hundreds of millions with profound but often under recognized neurological consequences. There are emerging lines of evidence establishing inflammation and immunity as the critical missing link connecting peripheral thyroid dysfunction to central nervous system manifestations. Thyroid hormones function as essential neuromodulators governing neurodevelopment, synaptic plasticity, and cognitive processing through integrated genomic and non-genomic mechanisms, with region-specific cerebral metabolic disturbances correlating with distinct neuropsychiatric symptoms. The immunological perspective reveals that AITD propagates neuroinflammation through convergent pathways: molecular mimicry enabling cross-reactivity between thyroid and neural antigens, cytokine-mediated disruption of neurotransmitter metabolism, HMGB1-driven glial activation, and blood-brain barrier compromise facilitating immune cell infiltration. The thyroid-gut-microbiota axis emerges as a critical mediator wherein dysbiosis perpetuates both thyroid autoimmunity and neuroinflammation through impaired serotonin precursor availability and increased intestinal permeability. Mitochondrial dysfunction represents an energetic common denominator, as thyroid hormone dysregulation directly impairs oxidative phosphorylation, producing region-specific cerebral metabolic disturbances. Simultaneous compromise of monoamine systems, cholinergic signaling abnormalities, and glutamate excitotoxicity creates a particularly toxic neurochemical state in untreated thyroid dysfunction. Common triggers such as psychological stress, gut dysbiosis, and mitochondrial impairment may activate interconnected pathways that simultaneously compromise thyroid and brain function, revealing that these disorders share fundamental mechanistic origins. These insights have been discussed in the current review to enhance the understanding of thyroid-brain function, the core mechanisms and consequences of functional deficits.

Journal Article↗
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

Artificial intelligence in treatment prediction for skeletal Class III malocclusion: A systematic review.

In skeletal Class III patients, treatment options range from orthodontics to orthognathic surgery. Choosing the optimal approach requires a comprehensive clinical evaluation, which may be supported by AI tools. The aim of this study was to assess the performance of AI models in predicting the need for orthognathic surgery and in identifying predictors influencing treatment decisions. A PRISMA-guided electronic database search (PubMed, Web of Science; 2009-2024; English/French) was performed to identify studies using machine learning (ML) or deep learning (DL) on cephalometric and clinical data. After screening and assessment for eligibility, 15 studies were critically appraised. Model performance was summarized using accuracy, sensitivity, specificity, and the area under the curve (AUC). ML algorithms (particularly Random Forest and XGBoost) and DL models (ResNet-based convolutional neural networks (CNNs)) achieved high accuracy for predicting surgical need. Frequently selected predictors included Wits appraisal, ANB angle, the maxillomandibular ratio (Mx/Md), overjet, and the divergence of the lower gonial angle. AI methods show promise for assisting treatment decisions in Class III malocclusion, with Random Forest and XGBoost performing well on tabular cephalometric data and CNNs on imaging. Larger, multicentre datasets and external validation are needed to improve reliability, address bias, and support clinical implementation.

Humans↗
PubMed2026

Mitophagy-mediated ferroptosis involved in 2,5-hexanedione-induced neurotoxicity in rats.

n-Hexane, a widespread environmental and industrial pollutant, poses serious health risks, particularly neurotoxicity. Chronic exposure primarily induces sensorimotor neuropathy via its metabolite 2,5-hexanedione (HD), yet the mechanisms underlying HD-induced neuronal injury remain unclear. Recent evidence implicates ferroptosis, an iron-dependent form of regulated cell death, in neurodegenerative processes. In this study, Sprague-Dawley (SD) rats were exposed to HD to establish a neuropathy model. Ferroptosis involvement was assessed using the iron chelator deferoxamine (DFO) and the ferroptosis inhibitor Ferrostatin-1. The potential role of mitophagy in HD-induced ferroptosis was evaluated by monitoring mitophagy markers and by autophagy inhibition with chloroquine (CQ). In vitro, SH-SY5Y cells were transfected with PINK-1 siRNA to explore mitophagy-mediated regulation of ferroptosis. HD exposure led to iron accumulation, lipid peroxidation, mitochondrial abnormalities, and decreased GPX4 in rat spinal neurons. DFO or ferrostatin-1 treatment ameliorated these changes and preserved mitochondrial integrity. Mechanistic analyses revealed HD-induced activation of mitophagy, as shown by upregulation of Beclin-1, LC3II, Drp-1, and PINK-1, with concomitant downregulation of P62 in spinal mitochondria. CQ suppressed mitophagy, reduced iron deposition and lipid peroxidation, and improved motor function. Similarly, PINK-1 knockdown in SH-SY5Y cells mitigated HD-induced mitophagy and ferroptosis. These findings demonstrate that HD induces neuronal ferroptosis via mitophagy activation. Inhibition of ferroptosis or mitophagy effectively attenuates HD-induced neurotoxicity, suggesting potential therapeutic strategies to reduce neural damage from environmental n-hexane exposure.

Animals↗
PubMed2026

Age-dependent reorganization of behavioral and striatal function in Cntnap2 knockout mice.

Autism spectrum disorder (ASD) is characterized by persistent deficits in social communication and the presence of restricted and repetitive behaviors. While ASD has a neurodevelopmental origin, it remains a lifelong condition, yet little is known about how its behavioral and neural features evolve across adulthood. Here, we investigated behavioral, synaptic, and structural alterations across the transition from early to mature adulthood in Cntnap2 knockout mice, a widely used model of ASD. Using a longitudinal behavioral approach combined with electrophysiological recordings and morphological analysis, we show that KO mice exhibit increased stereotyped and repetitive behaviors and reduced exploratory activity at both ages. However, detailed analysis of behavioral patterns revealed age-dependent differences, with early adult KO mice displaying increased behavioral persistence that later evolved into distinct patterns of behavioral sequences. These behavioral changes were associated with alterations in inhibitory synaptic transmission in the dorsolateral striatum (DLS), including changes in spontaneous inhibitory postsynaptic current (sIPSC) frequency and temporal structure. In parallel, mature adult KO mice showed structural remodeling of spiny projection neurons, characterized by increased distal dendritic arborization and age-dependent organization of dendritic spines. Together, our findings demonstrate that ASD-related alterations are not static but evolve across adulthood, revealing a multi-level reorganization of behavioral, synaptic, and structural features. These results highlight the importance of considering adulthood stages in ASD and provide new insights into the dynamic nature of the condition.

Animals↗
PubMed2026

Subacute and long-term changes in cognitive functioning after administration of classic psychedelics, MDMA and ketamine: A systematic review of clinical and preclinical evidence.

Psychedelic agents induce a window of heightened neuroplasticity that extends beyond acute intoxication, during which neural circuits are more amenable to change. This period may facilitate changes in cognition relevant to the treatment of psychiatric disorders. This systematic review synthesised clinical and preclinical evidence of subacute and long-term (≥1 day) effects of classic and non-classic psychedelics on cognition. MEDLINE, EMBASE, APA PsycInfo and Web of Science were searched to identify human and animal studies investigating psychedelics and cognition (executive function, attention, decision-making). Sixty-seven (47 clinical, 20 preclinical) articles met inclusion criteria. Psilocybin demonstrated the most consistent evidence of subacute and longer-term cognitive improvement, particularly in cognitive flexibility and attention. Ketamine showed enhancement across cognitive domains, although findings were heterogeneous. LSD and DMT showed no consistent subacute changes, while MDMA was associated with transient cognitive impairments that resolved within days. Risk of bias assessments revealed selective outcome reporting, poor reporting of missing data and inadequate methodological detail, limiting the confidence of findings. Current evidence provides preliminary support for subacute changes in cognition following administration of select psychedelic agents. Cognitive improvements were more frequently seen in psychiatric populations than healthy subjects, which may reflect a remediation of existing cognitive deficit rather than enhancement above normal functioning. Adequately powered, controlled studies with standardised reporting of cognitive outcomes are required to determine the magnitude, durability and clinical relevance of psychedelic-associated cognitive change.

Hallucinogens↗
PubMed2026

Association between prenatal exposure to tetrachloroethylene and adverse birth outcomes: Systematic review and meta-analysis.

BACKGROUND: Tetrachloroethylene (PCE) is a ubiquitous chlorinated solvent with documented placental transfer. Despite widespread environmental and occupational exposure, no prior systematic review has synthesized evidence on prenatal PCE exposure and adverse birth outcomes. METHODS: We conducted a systematic review and meta-analysis of observational studies. PubMed, Web of Science, PsycINFO, EMBASE, and CINAHL were searched from inception to July 13, 2026. Eligible studies reported associations between prenatal PCE exposure (drinking water or inhalation) and adverse birth outcomes. Study quality was assessed using the Newcastle-Ottawa Scale (NOS) and Agency for Healthcare Research and Quality (AHRQ) criteria. Random-effects meta-analyses were performed using risk ratios (RRs) with 95% confidence intervals (CIs), with Knapp-Hartung adjustments and Paule-Mandel τ2 estimation. RESULTS: Twenty one studies (1987-2023) met inclusion criteria. Prenatal PCE exposure was associated with spontaneous abortion (8 studies; RR = 1.28, 95% CI 1.00-1.63; I2 = 64.2%). Analyses of stillbirth, central nervous system defects, oral clefts, neural tube defects, preterm birth, low birthweight, and small-for-gestational-age (SGA) yielded positive but statistically non-significant pooled estimates. The certainty of evidence ranged from very low to low across outcomes (GRADE). CONCLUSIONS: Prenatal PCE exposure may be associated with spontaneous abortion, particularly at higher exposure levels, and with SGA. Findings support ongoing regulatory efforts to limit PCE in occupational and environmental settings, particularly for pregnant individuals. Future prospective studies with biological monitoring and confounder-adjusted designs are needed.

Tetrachloroethylene↗
PubMed2026

Neurocorrelates of nocturnal enuresis in pre-adolescent children.

INTRODUCTION: Nocturnal enuresis (NE) is a common neurodevelopmental condition, yet its underlying neural mechanisms remain unclear. This study leverages the large-scale Adolescent Brain Cognitive Development (ABCD) dataset to identify structural and functional brain correlates associated with active symptoms and the resolution of bedwetting. METHODS: Using cross-sectional data from 3472 participants aged 9-10 years, children were categorized into three groups: active nocturnal enuresis (ANE, n = 225), history of nocturnal enuresis (HNE, n = 1171), and healthy control groups (CG, n = 2076). Multimodal neuroimaging protocol evaluated macrostructural properties via structural MRI (sMRI), microstructural white matter integrity via diffusion MRI (dMRI), and functional connectivity via resting-state fMRI (fMRI). Group differences were evaluated using linear models within an ANCOVA framework, adjusting for intracranial volume and handedness with False Discovery Rate (FDR) correction. RESULTS: Compared to controls, the ANE group exhibited a significant volume deficit in the right caudate, decreased sulcal depth in the left insula, and lower internal correlation within the Cingulo-Opercular Network (CON). Conversely, the dry HNE group demonstrated significant structural adaptations, including bilaterally larger putamen volumes and increased right caudate volume compared to the ANE group. The HNE group also showed increased microstructural density (decreased mean diffusivity) in the bilateral hippocampus and an increased cortical surface area in the left insula. Both NE groups demonstrated persistently reduced functional coupling within the CON. CONCLUSIONS: Nocturnal enuresis appears to be associated with a potential complex central signaling deficits. Reduced internal correlation within the CON across both active and former bedwetters indicates a potential for impairment in processing internal homeostatic bladder signals during sleep.

Humans↗
PubMed2026

GPR3 in neuro-metabolic-immune-reproductive nexus - a potential therapeutic target for Multi-System diseases.

BACKGROUND: GPR3(G-protein-coupled receptor 3), an orphan G-protein-coupled receptor (GPCR) with constitutive Gs activity, is expressed in the brain, liver, ovary, and other tissues, regulating cell proliferation, differentiation, and apoptosis across the nervous, reproductive, immune, and metabolic systems. This review synthesizes evidence on its integrated signaling and physiological functions to address the lack of a comprehensive multisystem pathophysiology overview. METHODS: A systematic literature search was conducted on PubMed and Web of Science, using keywords such as "GPR3", "GPCR", "neurodegeneration", "metabolism", "immune", "reproduction", "agonist", "inhibitor", and "therapeutic target". This search identified GPR3's roles in neurodegenerative diseases, immune inflammation, reproduction, and energy metabolism. The analysis focused on signaling pathways, ligand regulation, and therapeutic potential. RESULTS: The research indicates that GPR3 is involved in neuronal survival, synaptic plasticity, and microglial activity via the cAMP/PKA, PI3K/Akt, and β - arrestin pathways. It promotes amyloid - β formation in Alzheimer's disease (AD), yet provides neuroprotection in Parkinson's disease (PD) models. It may contribute to anxiety/depression - like states, maintain oocyte meiotic arrest in the ovary, and activate thermogenic genes in adipose tissue. GPR3 modulates immune responses. Using oleic acid (OA) and diphenyleneiodonium (DPI) as activators, and AF64394 and cannabidiol (CBD) as antagonists, it shows potential in disease models. CONCLUSION: GPR3 acts as a central molecular hub integrating neural, metabolic, immune, and reproductive signaling, highlighting its potential as a therapeutic target for chronic multisystem disorders. However, its dual roles in certain pathologies and translation challenges necessitate further research.

Humans↗
PubMed2026

A systematic review and meta-analysis of visuospatial attentional deficits in Parkinson's patients.

Parkinson's disease (PD) is a neurodegenerative condition primarily characterized by motor deficits, yet cognitive impairments are increasingly recognized. While deficits in executive functioning are well documented even in the absence of cognitive decline, evidence of attentional deficits in PD remains inconsistent, and the role of motor symptom lateralization is unclear. In this systematic review and meta-analysis, we examined visual attention in right-handed, cognitively unimpaired idiopathic PD patients, focusing on the canonical attentional domains (sustained, selective, divided) and processes (alerting, endogenous and exogenous orienting, reorienting), as well as visuospatial bias. Four databases were searched for studies comparing PD patients with healthy controls. Meta-analytic estimates were derived using Hedges' g within random-effects models, and studies that could not be quantitatively integrated were summarized narratively. In addition, studies directly comparing patients with left- and right-predominant motor symptoms (LPD vs. RPD) were reviewed qualitatively. Across 51 studies, PD patients exhibited deficits in sustained, selective, and divided attention. Among attentional processes, only exogenous orienting was impaired, whereas alerting, endogenous orienting, and reorienting were preserved. Findings from the few studies examining visuospatial bias indicated small, context-dependent shifts in spatial attention rather than a consistent directional bias. These findings indicate that PD patients show visual-attentional impairments, particularly under high-demand conditions, while basic alertness and voluntary orienting appear preserved. Exogenous orienting deficits and subtle rightward spatial tendencies in LPD suggest disruption of right-hemisphere attentional networks. These results have implications for early cognitive assessment, rehabilitation strategies, and understanding the neural bases of attentional dysfunction in PD.

Humans↗
PubMed2026

Identification and characterization of G protein-coupled receptors in the nocturnal halictid bee Megalopta genalis.

G protein-coupled receptors (GPCRs) are one of the largest families of membrane proteins in insects, regulating vision, neural signal transduction, and various physiological behaviors. Megalopta genalis exhibits a unique facultatively eusocial lifestyle and possesses adaptations for nocturnal activity; however, its GPCR family has not yet been systematically characterized. In this study, we performed genome-wide identification, phylogenetic analysis, and expression profiling of GPCRs in M. genalis by integrating genomic annotation and transcriptomic analysis. The results showed that a total of 99 GPCRs were identified in the genome of M. genalis, which were classified into four major families. Here, we show that M. genalis has undergone lineage-specific GPCR repertoire remodeling, marked by the expansion of novel orphan receptors and the systematic loss of multiple receptor subtypes, such as the neuropeptide receptors MIP-R and NPFR. Moreover, opsins have formed a diverse array of combinations and non-GPCR odorant receptors have undergone significant expansion via tandem duplication. Together, these features may represent part of the molecular repertoire associated with the adaptation of M. genalis to a nocturnal lifestyle. Furthermore, transcriptomic analysis revealed distinct spatiotemporal expression divergence within each of the Mth/Mthl and Fz GPCR families, suggesting functional specialization across development and adult tissues. This study provides the first systematic identification and initial functional characterization of GPCRs in M. genalis, revealing an evolutionary pattern characterized by the coexistence of contraction and expansion within the GPCR family. These findings lay a foundation for further studies aimed at elucidating the roles of these GPCRs in regulating M. genalis physiology and behavior.

Animals↗
PubMed2026

Decoding the spatiotemporal patterns of food spoilage microbial communities: Integrating multi-omics and artificial intelligence to enable precision preservation.

In the global food supply chain, food wastage caused by spoilage has resulted in significant economic losses, food shortages, and environmental pressure. This process is fundamentally driven by the spatiotemporal dynamics of microbial communities. However, traditional research methods struggle to elucidate the complex mechanisms of spatial heterogeneity, interspecies interactions, and functional succession. This limits the development of effective preservation strategies. This review systematically reviews the cutting-edge progress of integrating multi-omics technologies and artificial intelligence (AI) to study food spoilage microbial communities, breaking through this bottleneck. We propose an intelligent theoretical framework that could potentially analyze microbial metabolic activities and predict dynamic shelf life if implemented. The conceptual framework integrates multidimensional data, including spatial metabolomics, temporal metatranscriptomics, single-cell transcriptomics, and longitudinal metagenomics. It can also be combined with AI models, such as graph neural networks. The article elaborates on the principles and applications of spatio-temporal monitoring technologies, such as nano secondary ion mass spectrometry, hyperspectral imaging, and the Internet of Things sensing. Through illustrative cases of typical perishable foods, it also explores how such a multi-omics - AI system might be applied to spoilage warning and precise intervention. Additionally, the article addresses the current challenges in data coverage, model generalization, and federated learning implementation. Then the research further explores emerging areas such as engineered probiotics, edge AI, and microfluidic sensing. These areas are targeted at transforming food preservation from an empirical control approach to a data-driven, precise regulatory framework. This transformation provides theoretical support and technical approaches for developing a smart, sustainable food preservation system.

Multiomics↗
PubMed2026

Predicting training outcomes for developmental dyslexia from EEG data.

Developmental dyslexia (DD) is characterised by lower-than-average reading abilities and is diagnosed in approximately 10% of individuals. The societal barriers may limit professional fulfilment and psychological wellbeing of individuals with DD, calling for the development of effective interventions to counteract them. As DD is associated with challenges in both phonological and visuo-attentional domains, different longitudinal training approaches were developed to strengthen them. However, they require a considerable amount of personal, social and economic resources and the outcomes may vary depending on individual differences in behavioural and neurophysiological functionality. Hence, predicting training outcomes might help in developing personalised treatment protocols and optimising the use of resources. In the present work we applied machine learning to resting-state EEG to predict longitudinal training outcomes in adults with DD enrolled in a randomized clinical trial. In particular, one group received a visuo-attentional training combined with transcranial alternating current stimulation (tACS), another group received visuo-attentional training with sham/placebo stimulation, and the third group received a phonological training with sham/placebo stimulation. The improvement in text reading speed was associated with spectral power in low-beta and individual frequencies in the alpha (IAF) and beta (IBF) bands, while the improvement in pseudoword reading was associated with IBF. The findings highlight the potential of capturing neural markers of treatment responsiveness in DD. Future studies should focus on the generalisability of predictive models to real-world settings, while investigating whether specific EEG markers predict responsiveness to distinct remediation protocols, thus supporting the development of personalised interventions.

Humans↗
PubMed2026

Systematic review of machine learning approaches for predicting sickle cell crisis and mortality risk at the climate-health nexus.

BACKGROUND: Sickle cell anemia (SCA) is a severe genetic blood disorder characterized by recurrent vaso-occlusive crises and increased mortality, with the greatest burden occurring in low- and middle-income countries. Climatic and environmental conditions, including temperature variability, humidity, rainfall, air pollution, and seasonal changes, have been associated with disease exacerbation. However, the extent to which these factors have been incorporated into predictive models remains unclear. This study systematically reviews the application of machine learning (ML) models for predicting SCA crises and mortality in relation to climate and environmental factors. METHODOLOGY: The PRISMA guidelines were used, and 34 peer-reviewed studies published between 2005 and 2026 were analyzed to identify the climate variables, ML approaches employed, and predictive performance. The reviewed studies applied a range of ML techniques, including artificial neural networks, random forests, support vector machines, decision trees, logistic regression, and deep learning models. Temperature, humidity, rainfall, wind speed, air quality indicators, and seasonal patterns were the most frequently examined environmental variables. RESULTS: The findings indicate that most existing models rely predominantly on clinical and demographic data, with limited integration of climate information and inadequate representation of high-burden regions, especially Sub-Saharan Africa. Studies incorporating environmental variables reported improved predictive performance and highlighted the potential of climate-informed early warning systems for SCA management. CONCLUSION: The review recommends development of interdisciplinary, climate-aware ML frameworks, expansion of longitudinal environmental datasets, and increased research in underrepresented regions to support climate-resilient and patient-centered SCA care.

Humans↗