Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 6 appraisals
Biomedical physics & engineering express
MELF: a multi-view ensemble learning framework for normative resting state EEG signal quality assessment
Normative resting-state electroencephalography (rsEEG), defined as standardized recordings from healthy populations, provides essential baselines for studying brain mechanisms and psychiatric disorders. However, its reliability critically depends on rigorous quality assessment, as artifacts or over-processing can distort subsequent analyses. Current approaches often rely on single-feature perspectives, which fail to capture the multidimensional nature of signal quality. We propose MELF, a quality assessment framework for normative rsEEG that systematically integrates three complementary feature domains: time domain (mobility, complexity), frequency domain (-band relative power, parallel log spectra index (PaLOSi)), and spatial domain (-band directed phase lag index, bad channel ratio, ICLabel-classified brain component proportion). A random forest-based ensemble classifier is constructed using dynamic weight allocation based on cross-validation accuracy (Acc). Experiments on four open rsEEG datasets (DSTR, TRCS, LEMON, and HBN) and expert-annotated clinical data demonstrate that: (1) MELF achieves up to 10% Acc improvement over single-view methods; (2) MELF outperforms traditional machine learning and deep learning baselines while reducing computational time, reaching 97.69% Acc (AUC = 0.9957), and maintains strong performance on an independent test set (92% Acc, AUC = 0.88); (3) the spectral view contributes the most, confirming the evaluative capability of the PaLOSi. MELF provides an interpretable, high-Acc, and robust automated framework for normative rsEEG quality assessment, with broad applications in neuroscience research and clinical quality control.
22 July 2026
Read appraisal →Cell reports methods
Spatial multi-omics imputation and embedding with SpaMIE
SpaMIE is a deep graph neural network framework designed to tackle the challenge of multi-section integration in spatial multi-omics (SMO) datasets with systematic missing modalities. Current SMO platforms face limitations such as high cost and limited throughput, leading to many large-scale spatial atlases relying on cost-effective mono-omics measurements while only a few sections are profiled with full multi-omics technologies. This results in heterogeneous modality coverage across tissue sections. SpaMIE offers a two-stage solution. In the first stage, it performs spatially informed cross-modal imputation, enabling accurate inference of missing modalities from mono-omics data. In the second stage, it integrates measured and imputed spatial multi-omics profiles across multiple tissue sections to learn a unified embedding. Benchmarking on simulated and experimental datasets shows that SpaMIE achieves accurate cross-modal imputation, robust multi-section integration, and improved spatial domain identification, providing a flexible and scalable solution for constructing and analyzing SMO atlases.
22 July 2026
Read appraisal →Neuroscience
Therapeutic efficacy of engineered exosomes in Alzheimer's disease: A systematic review and meta-analysis of preclinical animal models
Engineered exosomes are modified extracellular vesicles designed to enhance targeting and cargo delivery, and they have been proposed as a therapeutic strategy for Alzheimer's disease. We systematically reviewed preclinical animal studies evaluating engineered exosomes, synthesized evidence from comparisons with disease models and with natural exosomes, and reported the study in accordance with the PRISMA 2020 checklist. Outcomes included spatial learning and memory assessed by the Morris water maze, amyloid beta pathology, tau phosphorylation, and neuroinflammatory markers. Random effects meta-analyses suggested that engineered exosomes improved Morris water maze performance and reduced amyloid beta burden and pro-inflammatory cytokines compared with natural exosomes, whereas evidence regarding tau phosphorylation was limited and largely qualitative, and the overall certainty of evidence was low to very low. These findings support further investigation of engineered exosomes, but conclusions should be interpreted cautiously until confirmed by rigorously designed and blinded preclinical studies and clinical trials with standardized protocols.
25 May 2026
Read appraisal →Progress in biomedical engineering (Bristol, England)
Cross-subject generalization for EEG decoding: a survey of deep learning methods
Deep learning for cross-subject electroencephalography (EEG) decoding is hindered by high inter-subject variability, which introduces a severe domain shift between training and unseen test subjects. This survey presents a comprehensive review of deep learning methodologies specifically engineered to address this cross-subject generalization challenge. To ground this analysis, we formalize the cross-subject setting as a multi-source domain problem and delineate the rigorous, subject-independent evaluation protocols required for valid assessment. Central to this survey is a systematic taxonomy of the current literature into discrete methodological families, including feature alignment, adversarial learning, feature disentanglement, and contrastive learning. We conclude by examining three critical elements for advancing robust, real-world decoding: the theoretical limitations of current methodologies, the structural value of subject identity, and the emergence of EEG foundation models.
14 May 2026
Read appraisal →Angewandte Chemie (International ed. in English)
A Novel Acetylcholine Nanosensor for Single Vesicle Storage and Sub-Quantal Exocytosis in Living Neurons and Organoids
Acetylcholine (ACh) is a critical neurotransmitter that regulates diverse physiological functions, such as cognition and muscle contraction, through synaptic transmission. However, in situ quantitative chemical analysis of single-vesicle storage and release dynamics at single-cell level remains a major technical challenge, hindering mechanistic understanding of cholinergic synaptic plasticity across physiological and pathological states. To address this, we developed an Ti3C2Tx MXene/enzyme-functionalized (M@E@CF) nanosensor, enabling real-time monitoring of vesicular ACh storage and exocytotic release in primary cholinergic neurons and human spinal cord organoids. Our findings reveal a sub-quantal release mode in both mouse and human-derived neurons. To further elucidate the regulatory principles of exocytosis kinetics, we classified single-vesicle exocytosis patterns based on signal peak shapes using a 1D convolutional neural network (1D-CNN) deep learning model, uncovering significant differences in the number of released molecules and kinetic parameters across modes. Critically, in Down syndrome models, we observed significantly reduced single vesicle ACh storage and release alongside an elevated release fraction, concurrent with shortened fusion pore durations during exocytosis. Thus, the M@E@CF nanosensor platform establishes a versatile tool for spatiotemporal investigation of neurotransmitter storage and release dynamics, providing a critical technical foundation for exploring physiological functions and pathological mechanisms of the cholinergic system.
13 May 2026
Read appraisal →Neuron
Spatial and single-cell transcriptomic atlas of human suprachiasmatic nucleus
The suprachiasmatic nucleus (SCN) is considered the master pacemaker of the circadian clock in mammals, but our current knowledge of the SCN is mostly based on rodent studies. Here, we report a comprehensive molecular and cellular atlas for the adult human SCN by spatial transcriptomics, single-nucleus RNA sequencing, and deep-learning-based histological analysis. We identified seven human SCN neuron subtypes with specific transcriptomes and spatial distributions. Comparison of humans, mice, and non-human primates revealed the conserved functional segregation within the SCN regulated by LIM homeobox 1 (LHX1) and RAR-related orphan receptor B (RORB). Furthermore, our results suggested that the human SCN has undergone marked reorganization of its neuropeptide signaling network. Finally, integrative analysis of human SCN transcriptomes and genome-wide association studies (GWASs) identified arginine vasopressin (AVP)/neuromedin S (NMS) subtype as the potential neuronal correlate for morningness chronotype. Thus, our spatial and single-cell transcriptomic atlas of the human SCN provided a basis for the understanding of neural and molecular mechanisms of the human circadian clock.
18 Apr 2026
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