Molecular imaging holds the potential for noninvasive and accurate grading of liver fibrosis. It is limited by the lack of biomarkers that strongly correlate with liver fibrosis grade. Here, we discover the grading potential of fibroblast activation protein alpha (FAPα) for liver fibrosis through transcriptional analysis and biological assays on clinical liver samples. The protein and mRNA expression of FAPα are linearly correlated with fibrosis grade (R² = 0.89 and 0.91, respectively). A FAPα-responsive MRI molecular nanoprobe is prepared for quantitatively grading liver fibrosis. The nanoprobe is composed of superparamagnetic amorphous iron nanoparticles (AFeNPs) and paramagnetic gadoteric acid (Gd-DOTA) connected by FAPα-responsive peptide chains (ASGPAGPA). As liver fibrosis worsens, the increased FAPα cut off more ASGPAGPA, restoring a higher T₁-MRI signal of Gd-DOTA. Otherwise, the signal remains quenched due to the distance-dependent magnetic resonance tuning (MRET) effect between AFeNPs and Gd-DOTA. The nanoprobe identifies F1, F2, F3, and F4 fibrosis, with area under the curve of 99.8%, 66.7%, 70.4%, and 96.3% in patients’ samples, respectively. This strategy exhibits potential in utilizing molecular imaging for the early detection and grading of liver fibrosis in the clinic.

The current positive computed tomography (CT) contrast agents (PCTCAs) including clinical iodides, present high CT density value (CT‐DV). However, they are incapable for the accurate diagnosis of some diseases with high CT‐DV, such as osteosarcoma. Because bones and PCTCAs around osteosarcoma generate similar X‐ray attenuations. Here, an innovative strategy of negative CT contrast agents (NCTCAs) to reduce the CT‐DV of osteosarcoma is proposed, contributing to accurate detection of osteosarcoma. Hollow mesoporous silica nanoparticles, loading ammonia borane molecules and further modified by polyethylene glycol, are synthesized as NCTCAs for the diagnosis of osteosarcoma. The nanocomposites can produce H₂ in situ at osteosarcoma areas by responding to the acidic microenvironment of osteosarcoma, resulting in nearly 20 times reduction of CT density in osteosarcoma. This helps form large CT density contrast between bones and osteosarcoma, and successfully achieves accurate diagnosis of osteosarcoma. Meanwhile, The NCTCAs strategy greatly expands the scope of CT application, and provides profound implications for the precise clinical diagnosis, treatment, and prognosis of diseases.

Background: Cerebral small vessel disease (CSVD) is a cluster of microvascular disorders with unclear pathological mechanisms. The microbiota-gut-brain axis is an essential regulatory mechanism between gut microbes and their host. Therefore, the compositional and functional gut microbiota alterations lead to cerebrovascular disease pathogenesis. The current study aims to determine the alteration and clinical value of the gut microbiota in CSVD patients.Methods: Sixty-four CSVD patients and 18 matched healthy controls (HCs) were included in our study. All the participants underwent neuropsychological tests, and the multi-modal magnetic resonance imaging depicted the changes in brain structure and function. Plasma samples were collected, and the fecal samples were analyzed with 16S rRNA gene sequencing.Results: Based on the alpha diversity analysis, the CSVD group had significantly decreased Shannon and enhanced Simpson compared to the HC group. At the genus level, there was a significant increase in the relative abundances of Parasutterella, Anaeroglobus, Megasphaera, Akkermansia, Collinsella, and Veillonella in the CSVD group. Moreover, these genera with significant differences in CSVD patients revealed significant correlations with cognitive assessments, plasma levels of the blood-brain barrier-/inflammation-related indexes, and structural/functional magnetic resonance imaging changes. Functional prediction demonstrated that lipoic acid metabolism was significantly higher in CSVD patients than HCs. Additionally, a composite biomarker depending on six gut microbiota at the genus level displayed an area under the curve of 0.834 to distinguish CSVD patients from HCs using the least absolute shrinkage and selection operator (LASSO) algorithm.Conclusion: The evident changes in gut microbiota composition in CSVD patients were correlated with clinical features and pathological changes of CSVD. Combining these gut microbiota using the LASSO algorithm helped identify CSVD accurately.