Alzheimer’s disease (AD) is a degenerative condition affecting the central nervous system and is the primary cause of dementia. Current therapies for AD are ineffective. Although brain regeneration via stem cell transplantation has therapeutic potential, suitable sources are limited. Hair follicle stem cells (HFSCs) are multi-potent cells and can differentiate into mesodermal and ectodermal lineages, and proliferate for extended periods. Nerve growth factor (NGF) is a neurotrophin that is vital for neuronal development and survival, and the regulation of apoptosis in neurodegenerative disorders. However, using HFSCs to treat AD has not been extensively investigated. Herein, we evaluated the therapeutic effects of HFSCs and the synergistic effect of NGF and HFSCs on AD.

A rat model of AD was established by intrahippocampal injection of amyloid β-protein 1–42 (Aβ1–42). After 14 days, HFSCs and HFSCs overexpressing NGF were injected into the hippocampus of AD rats for therapy. The cognitive function of the treated AD rats was tested using the Morris water maze test. Congo red staining, immunohistochemistry, and enzyme-linked immunosorbent assay (ELISA) were used to detect deposition, as well as soluble Aβ1–40 and Aβ1–42 levels. Additionally, western blotting was used to assess tau protein, the phosphoinositide-3 kinase (PI3K)/protein kinase B/glycogen synthase kinase-3β (Akt/GSK-3β) pathway, and the levels of synapse proteins.

HFSCs and HFSCs/NGF transplantation not only significantly reduced Aβ deposition but also inhibited GSK-3β activity and reduced tau protein hyperphosphorylation by stimulating the PI3K/Akt signaling pathway. Moreover, HFSC and HFSC/NGF transplantation led to significant overexpression of the synaptophysin (SYP) and postsynaptic density protein 95 (PSD95) in the hippocampus of AD rats.

HFSCs and NGF-modified HFSCs may become a promising treatment option for AD.

The stage-specific dynamics of functional brain networks in early Parkinson’s disease cognitive impairment (PD-CI) remain unclear. This study investigated caudate-centric hierarchical functional network reconfiguration across early PD-CI stages using simultaneous [¹⁸F]fluoropropyl-(+)-dihydrotetrabenazine positron emission tomography (¹⁸F-FP-DTBZ PET) and resting-state functional magnetic resonance imaging (rs-fMRI).

Forty-six Parkinson’s disease (PD) patients underwent simultaneous ¹⁸F-FP-DTBZ PET/MR with rs-fMRI sequences. Patients were categorized as normal cognition (PD-NC, n = 15), subjective cognitive decline (PD-SCD, n = 16), and mild cognitive impairment (PD-MCI, n = 15). PET-identified striatal regions with significant dopaminergic deficits were used as seeds for stepwise functional connectivity (SFC) analysis. Associations with cognitive factors and network coupling in early PD-CI were evaluated.

¹⁸F-FP-DTBZ PET revealed that the caudate nucleus was a critical dopaminergic hub in early PD-CI. Caudate seed-based SFC analysis revealed a triphasic reconfiguration: stable integration in PD-NC, compensatory hyperconnectivity in PD-SCD, and global inefficiency with rigidity in PD-MCI. Key circuits showed reduced connectivity in PD-MCI including caudate linkages with the globus pallidus, thalamus, right superior frontal gyrus, left inferior temporal gyrus, right superior orbitofrontal cortex, supplementary motor area, and right hippocampus. Clinical analysis showed that both global cognitive efficiency and memory control were associated with specific short- and long-range caudate connectivity.

The caudate nucleus is central to the interplay between dopaminergic metabolic deficits and functional network reconfiguration during early PD-CI progression, shifting from compensatory hyperconnectivity to network rigidity. These findings provide a mechanistic framework for targeted neuromodulation strategies in early PD-CI.

White-matter hyperintensities (WMHs) are a signature feature of cerebral small-vessel disease and are associated with cognitive decline. This study used network-based statistics (NBS) to investigate global functional network changes and their association with cognitive function in individuals with WMHs.

The Montreal Cognitive Assessment (MoCA) was administered to 33 individuals with WMHs and 34 healthy controls. Whole-brain resting-state functional-connectivity (RSFC) differences were analyzed using NBS on resting-state functional Magnetic Resonance Imaging data. Significant connectivity of modular changes within and between networks was examined, and the relationship between MoCA and RSFC was analyzed. Support vector machine (SVM) models were used to evaluate the potential of functional networks as a supplement to structural imaging and a sensitive subclinical indicator.

Individuals with WMHs exhibited significantly lower MoCA scores than did healthy controls. Inter-regional RSFC analysis revealed reduced connectivity across some networks, including the Default Mode Network–Sensorimotor Network (DMN–SMN), DMN–Cingulo-Opercular Network (DMN–CON), and CON–Cerebellar Network (CON–CER). The SVM models demonstrated robust classification performance, with areas under the curve (AUC) of 0.864 ± 0.155 for DMN–SMN, 0.838 ± 0.175 for DMN–CON, and 0.821 ± 0.167 for CON–CER. Global RSFC strength and modular RSFC strength were positively correlated with MoCA scores.

WMHs are associated with widespread RSFC alterations, especially in networks involved in cognition and motor control; these differences may contribute to cognitive decline in WMHs and serve as potential biomarkers for early diagnosis and intervention.

Although pressing intervention is widely used clinically to alleviate pain associated with chronic myofascial trigger points (MTrPs), the mechanisms by which it modulates pain via sensory nerves remain unclear. This study aimed to investigate the effects of pressing intervention on transient receptor potential vanilloid 1 (TRPV1) channels in sensory nerves and to explore its potential analgesic mechanisms.

Twenty-six male Sprague-Dawley rats were randomly divided into a blank group (n = 6) and a model establishment group (n = 20). Chronic MTrPs were induced in the model group by blunt strike combined with eccentric exercise. Eighteen successfully prepared were randomized into model, press, and press + capsaicin (TRPV1 agonist) groups (n = 6 per group). The press group received local pressing at MTrPs every two days for seven sessions, while the press + capsaicin group received intraperitoneal capsaicin prior to pressing. Pressure pain threshold (PPT) and soft tissue tension (STT), with STT quantified by the displacement at a loading force of 0.2 kg (D_0.2), were measured before and after intervention. After treatment, the MTrPs tissue and the L2–L4 dorsal root ganglia (DRG) were collected for analysis. Skeletal muscle morphology was observed by hematoxylin-eosin (HE) staining. Inflammatory mediators in MTrPs tissue were measured by enzyme-linked immunosorbent assay (ELISA). TRPV1 protein expression in DRG was detected by Western blot. Immunofluorescence was used to detect TRPV1 on CGRP⁺ fibers in MTrPs and to quantify TRPV1⁺/c-Fos⁺ cells in DRG.

Compared with the blank group, the model group showed reduced pain threshold, increased soft tissue tension, disorganized myocytes, inflammatory infiltration, elevated TRPV1 in nerve endings, increased interleukin-1β (IL-1β), prostaglandin E2 (PGE₂), calcitonin gene-related peptide (CGRP), substance P (SP), decreased interleukin-10 (IL-10), and upregulated TRPV1 and TRPV1⁺/c-Fos⁺ cells in DRG (p < 0.01 or p < 0.05). Pressing reversed these changes, restored the pain threshold, soft tissue tension, and myocyte morphology, reduced TRPV1 and pro-inflammatory mediators, increased IL-10, and downregulated TRPV1 and TRPV1⁺/c-Fos⁺ cells in DRG (p < 0.01 or p < 0.05). These effects were partially blocked by capsaicin, as the press + capsaicin group exhibited reversed effects compared with pressing alone (p < 0.01 or p < 0.05).

Pressing intervention increases the mechanical pain threshold and improves soft tissue tension in rats with MTrPs. The underlying mechanism may be associated with alleviating local inflammation, modulating the TRPV1 channel in unmyelinated C-type sensory nerve fibers, and inhibiting TRPV1 expression, thereby reducing sensory nerve excitability.

Dorsal repellent axon guidance protein (draxin) is a secreted protein that plays an establishment role in the formation of proper connections between neurons. Although draxin is known to regulate the elongation of axons from various types of neurons in vitro, its specific role in mature neurons remains unclear. Draxin expression in the hippocampal region of patients with Alzheimer’s disease (AD) has been reported to be higher than in normal subjects. The present study investigated the effect of draxin on the expression of microtubule-associated protein 2 (MAP2) and neuronal nuclear antigen (NeuN), and on tau protein phosphorylation in mouse hippocampal neurons (HT22 cells) and AD cellular models. In addition, stereotactic techniques were used to inject neuronal-targeted adeno-associated virus (AAV) into the hippocampus of C57BL/6 mice to assess the effects of draxin overexpression on hippocampal neurons, as well as on behavioral and pathological features.

In vitro experiments were conducted using mouse hippocampal neuronal cells (HT22 cells) and established AD cellular models, focusing on evaluating draxin’s effects on the expression of neuronal markers (MAP2 and NeuN) and the phosphorylation status of tau protein. For in vivo validation, neuron-targeted AAV was delivered into the hippocampus of C57BL/6 mice via brain stereotactic injection to achieve draxin overexpression. Subsequent assessments included analyses of hippocampal neuronal integrity, behavioral tests (Y-maze and Morris water maze, to evaluate spatial learning and memory), and detection of AD-related pathological markers.

In vitro experiments revealed that draxin overexpression decreased the cell survival rate, increased the apoptosis rate, decreased the expression of MAP2 and NeuN, and showed a trend for increased phosphorylation of tau protein compared with the control group. Notably, the spatial learning memory ability of mice with draxin overexpression in the brain, as determined by the Y-maze and Morris water maze tests, was significantly diminished compared with the control group. These mice also showed elevated tau protein phosphorylation and altered expression of wingless-related integration site (Wnt)/β-catenin/glycogen Synthase Kinase 3 beta (GSK-3β) pathway components.

Our results suggest for the first time that neuronal overexpression of draxin may induce neuronal damage via the Wnt/β-catenin/GSK-3β signaling pathway, leading to AD-like neuropathological damage and cognitive dysfunction.

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by diverse presentations, which complicates the identification of consistent biological markers. This study examined whether integrating multimodal neuroimaging and physical-health measures from a population-based cohort can improve ASD classification and reveal interpretable markers that reflect both clinical and community variation.

Data were drawn from the Adolescent Brain Cognitive Development (ABCD) Study, a large community-based cohort of adolescents recruited from the general population. Participants with and without ASD were selected from this cohort, allowing contrasts that reflect natural variability across individuals. Structural, diffusion, and resting-state functional magnetic resonance imaging (MRI) data were integrated with physical-health markers, including sleep, growth, and early development. Propensity-score matching created demographically balanced groups, and multimodal machine learning models were evaluated through stratified cross-validation.

The multimodal integration of brain and physical-health markers outperformed single-modality models (area under the receiver operating characteristic curve [AUC-ROC] = 0.68, 95% confidence interval [CI]: 0.62–0.73; area under the precision-recall curve [AUC-PR] = 0.66, 95% CI: 0.60–0.73). Among physical-health markers, sleep function contributed most strongly to ASD classification, while neuroimaging predictors included cortical thickness in the right superior temporal gyrus and connectivity between the cingulo-opercular and default mode networks. These findings indicate that integrating modalities capturing both neural and physiological systems provides complementary information for identifying ASD-related differences within a population-based framework.

This study provides a proof of concept that combining multimodal MRI and physical-health data within a large, demographically representative cohort can enhance ASD classification and yield biologically interpretable features. The population-based design situates these findings within a community context and offers a preliminary framework for integrating neural and physiological measures in future large-scale studies of neurodevelopmental diversity.

To examine potential differences in electroencephalogram (EEG) dynamic functional connectivity between patients with major depressive disorder (MDD) and healthy controls (HC), and thereby enhance the effectiveness of depression identification.

This study presents a novel approach that combines EEG microstate analysis with functional connectivity networks. Resting-state 19-channel EEG data were obtained from 36 participants (17 healthy controls and 19 patients with depression). Through microstate analysis, significant inter-group differences were observed in the average durations of microstates A and C. Subsequently, EEG segments corresponding to microstate classes A and C were extracted. Following the surface Laplacian transformation, the phase locking value (PLV) was applied to construct functional connectivity networks, and their topological characteristics were extracted. Based on the analysis of network indicators (node degree, clustering coefficient, local efficiency, and global efficiency), global and nodal features showing significant group differences were screened and fused with equal weighting. The classification performance of the fused features and individual features was then assessed using three models: Support Vector Machine (SVM), Backpropagation Neural Network (BP), and K-Nearest Neighbors (KNN).

The findings indicate that network features derived from microstate C exhibited higher discriminative ability. Across all classification models, node degree features consistently outperformed other individual topological attributes in recognition accuracy, with the KNN model achieving the highest average accuracy of 96.48%. Furthermore, the fused feature set, incorporating more comprehensive EEG information, showed improved classification performance across all models, exceeding the results obtained using any single feature. The average accuracy reached 97.35% under different model configurations.

Dynamic analysis of brain networks can effectively distinguish patients with depression from healthy controls. This study not only provides a basis for exploring dynamic activities of brain regions associated with depression, but also offers potential objective physiological indicators for disease diagnosis.