Given that cancer mortality is usually due to a late diagnosis, early detection is crucial to improve the patient’s results and prevent cancer-related death. Imaging technology based on novel nanomaterials has attracted much attention for early-stage cancer diagnosis. In this study, a new block copolymer, poly(ethylene glycol)-poly(l-lactide) diblock copolymer (PEG-PLLA), was synthesized by the ring-opening polymerization method and thoroughly characterized using Fourier transform infrared spectroscopy (FT-IR), proton nuclear magnetic resonance spectroscopy (H-NMR), X-ray diffraction (XRD), and thermogravimetric analysis (TGA). The obtained PEG-PLLA was used to prepare nanoparticles encapsulated with perfluoropentane and salicylic acid by the emulsion-solvent evaporation method, resulting in a new dual-mode nano-image probe (PEG-PLLA@SA·PFP). The zeta potential and mean diameter of the obtained nanoparticles were measured using dynamic light scattering (DLS) with a Malvern Zetersizer Nano. The in vitro biocompatibility of the PEG-PLLA nanoparticles was evaluated with cell migration, hemolysis, and cytotoxicity assays. Ultrasonic imaging was performed using an ultrasonic imaging apparatus, and chemical exchange saturation transfer (CEST) MRI was conducted on a 7.0 T animal scanner. The results of IR and NMR confirmed that the PEG-PLLA was successfully synthesized. The particle size and negative charge of the nanoparticles were 223.8 ± 2.5 nm and −39.6 ± 1.9 mV, respectively. The polydispersity of the diameter was 0.153 ± 0.020. These nanoparticles possessed good stability at 4 °C for about one month. The results of cytotoxicity, cell migration, and hemolysis assays showed that the carrier material was biocompatible. Finally, PEG-PLLA nanoparticles were able to significantly enhance the imaging effect of tumors by the irradiation of ultrasound and saturation by a radiofrequency pulse, respectively. In conclusion, these nanoparticles exhibit promising dual-mode capabilities for US/CEST MR imaging.

Background: Medical imaging modalities, such as magnetic resonance imaging (MRI), ultrasound, and fluorescence imaging, have gained widespread acceptance in clinical practice for tumor diagnosis. Each imaging modality has its own unique principles, advantages, and limitations, thus necessitating a multimodal approach for a comprehensive disease understanding of the disease process. To enhance diagnostic precision, physicians frequently integrate data from multiple imaging modalities, driving research advancements in multimodal imaging technology research. Methods: In this study, hematoporphyrin-poly (lactic acid) (HP-PLLA) polymer was prepared via ring-opening polymerization and thoroughly characterized using FT-IR, ¹H-NMR, XRD, and TGA. HP-PLLA based nanoparticles encapsulating perfluoropentane (PFP) and salicylic acid were prepared via emulsion-solvent evaporation. Zeta potential and mean diameter were assessed using DLS and TEM. Biocompatibility was evaluated via cell migration, hemolysis, and cytotoxicity assays. Ultrasonic imaging was performed with a dedicated apparatus, while CEST MRI was conducted using a 7.0 T animal scanner. Results: We designed and prepared a novel dual-mode nanoimaging probe SA/PFP@HP-PLLA NPs. PFP enhanced US imaging, while salicylic acid bolstered CEST imaging. With an average size of 74.43 ± 1.12 nm, a polydispersity index of 0.175 ± 0.015, and a surface zeta potential of − 64.1 ± 2.11 mV. These NPs exhibit excellent biocompatibility and stability. Both in vitro and in vivo experiments confirmed the SA/PFP@HP-PLLA NP’s ability to improve tumor characterization and diagnostic precision. Conclusion: The SA/PFP@HP-PLLA NPs demonstrate promising dual-modality imaging capabilities, indicating their potential for preclinical and clinical use as a contrast agent.

Objective: Insomnia is the most common type of sleep disorder; however, the neurobiological causes and correlates of hyperarousal in insomnia remain to be fully determined, and the levels of cerebral metabolites in patients with insomnia remain unclear. This study aimed to quantify changes in cerebral metabolite levels in patients with insomnia and to explore their relationship with fatigue, anxiety, and subjective sleepiness. Methods: Twenty unmedicated patients with insomnia disorder and 21 age- and sex-matched healthy volunteers were included. The concentrations of metabolites including γ -aminobutyric acid (GABA+), glutamate (Glu), glycerophosphocholine (GPC), creatine (Cr), and phosphocreatine (PCr) were obtained by magnetic resonance spectroscopy, and the differences in metabolites between the two groups were compared. Sleep quality, sleepiness, anxiety, and fatigue were assessed using the Pittsburgh Sleep Quality Index (PSQI), Karolinska Sleepiness Scale (KSS), Beck Anxiety Inventory (BAI), and Fatigue Severity Scale (FSS), respectively. Correlations between the changes in GABA+, Glu, and GPC levels and the PSQI, KSS, FSS, and BAI scores were evaluated in patients with insomnia. Results: GABA+ levels were significantly lower in patients with insomnia than in healthy controls ( p = 0.027), whereas GPC and Cr+PCr levels were significantly higher ( p < 0.001 and p = 0.003, respectively). However, Glu levels were comparable between the groups ( p = 0.962). Furthermore, GABA+ levels were significantly negatively correlated with FSS (r = −0.656, p = 0.003) and BAI (r = −0.467, p = 0.038) scores; a trend-level negative association with KSS was also observed (r = −0.419, p = 0.066). Conclusion: Our results revealed alterations in the levels of GABA+ and GPC in the thalamus of patients with insomnia. These findings provide objective neurochemical evidence for the pathophysiological mechanisms of insomnia.

Vascular mild cognitive impairment (VMCI) is an early and reversible stage of dementia. Volume differences in regional gray matter may reveal the development and prognosis of VMCI. This study selected 2 of the most common types of VMCI, namely, periventricular white matter hyperintensities (PWMH, n = 14) and strategic single infarctions (SSI, n = 10), and used the voxel-based morphometry method to quantify their morphological characteristics. Meanwhile, age- and sex-matched healthy volunteers were included (n = 16). All the participants were neuropsychologically tested to characterize their cognitive function and underwent whole-brain magnetic resonance imaging scanning. Our results showed that the volumes of the bilateral temporal lobes and bilateral frontal gray matter were obviously diminished in the PWMH group. The atrophy volume difference was 4,086 voxels in the left temporal lobe, 4,154 voxels in the right temporal lobe, 1,718 voxels in the left frontal lobe, and 1,141 voxels in the right frontal lobe (P ≤ 0.001). Moreover, the characteristics of the gray matter atrophy associated with the PWMH were more similar to those associated with Alzheimer’s disease than SSI, which further revealed the susceptibility for escalation from PWMH to dementia. In conclusion, PWMH patients and SSI patients have different morphological characteristics, which explain the different prognoses of VMCI.

Background: Digital inclusion has become increasingly important for healthy aging, yet evidence on its association with depression among older adults remains limited, particularly with respect to potential cognitive heterogeneity. Objective: This study examined the association between digital inclusion and depressive symptoms among Chinese older adults and explored whether cognitive function moderates this association, as well as potential mediating pathways. Methods: Data were drawn from the 2020 wave of the China Health and Retirement Longitudinal Study, including 9,111 individuals aged 60 years and older. A multidimensional digital inclusion index was constructed based on access, use, and skills related to digital technologies, standardized to a 0–10 scale. Depressive symptoms were assessed using the 10-item Center for Epidemiological Studies Depression Scale (CES-D-10; each item scored 0–3, total range 0–30, treated as a continuous score). Multi-variable regression models were used to examine associations, with interaction terms and Johnson–Neyman analysis applied to assess moderation by cognitive function, with effect size estimated using Cohen’s f ² . Structural equation modeling was conducted to explore potential mediating mechanisms. Results: Cognitive function significantly moderated the association between digital inclusion and depressive symptoms (β = −0.002, P < 0.05). Among participants in the highest quartile of cognitive function, each unit increase in digital inclusion was associated with a 0.517-point decrease in depression scores, whereas no statistically significant association was observed among those in the lowest quartile (β = −0.137, P = 0.327). Pathway analyses suggested that the association operated through multiple pathways, with the direct pathway accounting for 66.7% of the total association—likely reflecting immediate psychological mechanisms including enhanced self-efficacy, expanded access to health information, and a sense of digital mastery—while cognitive-related and social participation pathways explained 8.3% and 8.0%, respectively; a sequential cognitive–social pathway accounted for a further 2.8%, and the remaining 14.2% was attributable to other model-estimated pathways, with all pathways together accounting for 100% of the total association. Executive function and immediate memory emerged as the cognitive domains most strongly associated with moderation patterns. Conclusions: These findings suggest that greater digital inclusion is associated with lower depressive symptoms among older adults and that cognitive function shapes heterogeneity in this association. Enhancing digital inclusion while accounting for cognitive differences may help inform digital health strategies aimed at promoting mental well-being in aging populations.