The highly sensitive detection and imaging of biomarkers are critical for early diagnosis, treatment, and prognosis monitoring. The unique size and structure of fluorescent nanomaterials provide key benefits such as excellent photostability, high fluorescence quantum yield, and tunable excitation and emission wavelengths. These properties have led to the widespread application of nanomaterials in fluorescent biomarkers detection and imaging. In this review, we began by introducing the composition of fluorescent probes and discussing the underlying sensing mechanisms. We then summarized recent advances in the use of fluorescent nanomaterials such as quantum dots (QDs), metal nanoclusters (MNCs), carbon dots (CDs), and metal-organic frameworks (MOFs) for biomarker detection and imaging. Additionally, we highlighted the applications of fluorescent nanomaterials in the detection and imaging of small molecules, biomacromolecules, and various biomarkers, including metal ions, bacteria, and circulating tumor cells (CTCs). The challenges and future prospects of fluorescent nanomaterials in biomarker detection and imaging were also discussed. We anticipate that fluorescent nanomaterials will have profound implications for clinical biomarker detection and imaging, with considerable application in both academic research and industrial applications.

Stroke, also known as cerebrovascular accident, is an acute cerebrovascular disease with a high incidence, disability rate, and mortality. It can disrupt the interaction between the cerebral cortex and external muscles. Corticomuscular coherence (CMC) is a common and useful method for studying how the cerebral cortex controls muscle activity. CMC can expose functional connections between the cortex and muscle, reflecting the information flow in the motor system. Afferent feedback related to CMC can reveal these functional connections. This paper aims to investigate the factors influencing CMC in stroke patients and provide a comprehensive summary and analysis of the current research in this area. This paper begins by discussing the impact of stroke and the significance of CMC in stroke patients. It then proceeds to elaborate on the mechanism of CMC and its defining formula. Next, the impacts of various factors on CMC in stroke patients were discussed individually. Lastly, this paper addresses current challenges and future prospects for CMC.

In schizophrenia, executive dysfunction is the most critical cognitive impairment, and is associated with abnormal neural activities, especially in the frontal lobes. Complexity estimation using electroencephalogram (EEG) recording based on nonlinear dynamics and task performance tests have been widely used to estimate executive dysfunction in schizophrenia. The present study estimated the cool executive function based on fractal dimension (FD) values of EEG data recorded from first-episode schizophrenia patients and healthy controls during the performance of three cool executive function tasks, namely, the Trail Making Test-A (TMT-A), Trail Making Test-B (TMT-B), and Tower of Hanoi tasks. The results show that the complexity of the frontal EEG signals that were measured using FD was different in first-episode schizophrenia patients during the manipulation of executive function. However, no differences between patients and controls were found in the FD values of the EEG data that was recorded during the performance of the Tower of Hanoi task. These results suggest that cool executive function exhibits little impairment in first-episode schizophrenia patients.

Dopamine (DA) and uric acid (UA) are essential for many physiological processes in the human body. Abnormal levels of DA and UA can lead to multiple diseases, such as Parkinson’s disease and gout. In this work, a three-dimensional reduced graphene oxide–MXene (3D rGO-Ti₃C₂) composite electrode was prepared using a simple one-step hydrothermal reduction process, which could separate the oxidation potentials of DA and UA, enabling the simultaneous detection of DA and UA. The 3D rGO-Ti₃C₂ electrode exhibited excellent electrocatalytic activity towards both DA and UA. In 0.01 M PBS solution, the linear range of DA was 0.5–500 µM with a sensitivity of 0.74 µA·µM⁻¹·cm⁻² and a detection limit of 0.056 µM (S/N = 3), while the linear range of UA was 0.5–60 µM and 80–450 µM, with sensitivity of 2.96 and 0.81 µA·µM⁻¹·cm⁻², respectively, and a detection limit of 0.086 µM (S/N = 3). In 10% fetal bovine serum (FBS) solution, the linear range of DA was 0.5–500 µM with a sensitivity of 0.41 µA·µM⁻¹·cm⁻² and a detection limit of 0.091 µM (S/N = 3). The linear range of UA was 2–500 µM with a sensitivity of 0.11 µA·µM⁻¹·cm⁻² and a detection limit of 0.6 µM (S/N = 3). The modified electrode exhibited advantages such as high sensitivity, a strong anti-interference capability, and good repeatability. Furthermore, the modified electrode was successfully used for DA measurement in vivo. This could present a simple reliable route for neurotransmitter detection in neuroscience.

Stroke is one of the leading causes of adult disability, often resulting in motor dysfunction and brain network reorganization. Brain-computer interface (BCI) systems offer a novel approach to post-stroke motor rehabilitation, with motor imagery (MI) serving as a key paradigm that requires decoding left and right-hand MI differences to optimize system performance. However, the neural dynamics underlying these differences, especially from the perspective of Electroencephalography(EEG) microstate, remain poorly understood in acute stroke patients. This study enrolled 14 acute stroke patients and recorded their EEG data during left and right-hand MI tasks. Four EEG microstate (A, B, C, and D) were analyzed to extract temporal feature parameters, including Duration, Occurrence Coverage, and transition probabilities(TP). Significant features were used to construct classification models using Linear Discriminant Analysis(LDA), Support Vector Machines(SVM), and K-Nearest Neighbors(KNN) algorithms. Microstate analysis revealed significant differences in temporal features of microstate A and C during left and right-hand MI tasks. During left-hand MI, microstate A exhibited longer Duration(Pfdr=0.032), higher Occurrence(Pfdr=0.018), and greater Coverage(Pfdr=0.004) compared to the right-hand, whereas microstate C showed the opposite pattern(Pfdr=0.044, Pfdr=0.004, Pfdr=0.004). Additionally, the TP from microstate B→A, D→A and D→C also demonstrated significant differences(Pfdr=0.04, Pfdr<0.001, Pfdr=0.006). Among classification models, the KNN algorithm achieved the highest accuracy of 75.00%, outperforming LDA and SVM. Fisher analysis indicated that the Occurrence of microstate C was the most discriminative feature for distinguishing between left and right-hand MI tasks in acute stroke patients. Differences in EEG microstate features during left and right-hand MI tasks in acute stroke patients may reflect lateralized mechanisms of brain network reorganization. Microstate features hold significant potential for both post-stroke brain function assessment and the optimization of BCI systems. These features could enhance adaptive BCI strategies in acute stroke rehabilitation.