Quantitative imaging for predicting hematoma expansion in intracerebral hemorrhage: A multimodel comparison.

BACKGROUND: Several studies report that radiomics provides additional information for predicting hematoma expansion in intracerebral hemorrhage (ICH). However, the comparison of diagnostic performance of radiomics for predicting revised hematoma expansion (RHE) remains unclear. METHODS: The cohort comprised 312 consecutive patients with ICH. A total of 1106 radiomics features from seven categories were extracted using Python software. Support vector machines achieved the best performance in both the training and validation datasets. Clinical factors models were constructed to predict RHE. Receiver operating characteristic curve analysis was used to assess the abilities of non-contrast computed tomography (NCCT) signs, radiomics features, and combined models to predict RHE. RESULTS: We finally selected the top 21 features for predicting RHE. After univariate analysis, 4 clinical factors and 5 NCCT signs were selected for inclusion in the prediction models. In the training and validation dataset, radiomics features had a higher predictive value for RHE (AUC = 0.83) than a single NCCT sign and expansion-prone hematoma. The combined prediction model including radiomics features, clinical factors, and NCCT signs achieved higher predictive performances for RHE (AUC = 0.88) than other combined models. CONCLUSIONS: NCCT radiomics features have a good degree of discrimination for predicting RHE in ICH patients. Combined prediction models that include quantitative imaging significantly improve the prediction of RHE, which may assist in the risk stratification of ICH patients for anti-expansion treatments.

Chronoamperometric interrogation of an electrochemical aptamer-based sensor with tetrahedral DNA nanostructure pendulums for continuous biomarker measurements.

Tetrahedral DNA nanostructure (TDN) is highly promising in developing electrochemical aptamer-based (E-AB) sensors for biomolecular detection, owing to its inherit programmability, spatial orientation and structural robustness. However, current interrogation strategies applied for TDN-based E-AB sensors, including enzyme-based amperometry, voltammetry, and electrochemical impedance spectroscopy, either require complicated probe design or suffer from limited applicability or selectivity. In this study, a TDN pendulum-empowered E-AB sensor interrogated by chronoamperometry for reagent-free and continuous monitoring of a blood clotting enzyme, thrombin, was developed. TDN pendulums with extended aptamer sequences at three vertices were immobilized on a gold electrode via a thiolated double-stranded DNA (dsDNA) at the fourth vertex, and their motion is modulated by the bonding of target thrombin to aptamers. We observed a significantly amplified signalling output on our sensor based on the TDN pendulum compared to E-AB sensors modified with linear pendulums. Moreover, our sensor achieved highly selective and rapidly responsive measurement of thrombin in both PBS and artificial urine, with a wide dynamic range from 1 pM to 10 nM. This study shows chronoamperometry-enabled continuous biomarker monitoring on a sub-second timescale with a drift-free baseline, demonstrating a novel approach to accurately detect molecular dynamics in real time.

LVONet: automatic classification model for large vessel occlusion based on the difference information between left and right hemispheres.

Objective.Stroke is a highly lethal condition, with intracranial vessel occlusion being one of its primary causes. Intracranial vessel occlusion can typically be categorized into four types, each requiring different intervention measures. Therefore, the automatic and accurate classification of intracranial vessel occlusions holds significant clinical importance for assessing vessel occlusion conditions. However, due to the visual similarities in shape and size among different vessels and variations in the degree of vessel occlusion, the automated classification of intracranial vessel occlusions remains a challenging task. Our study proposes an automatic classification model for large vessel occlusion (LVO) based on the difference information between the left and right hemispheres.Approach.Our approach is as follows. We first introduce a dual-branch attention module to learn long-range dependencies through spatial and channel attention, guiding the model to focus on vessel-specific features. Subsequently, based on the symmetry of vessel distribution, we design a differential information classification module to dynamically learn and fuse the differential information of vessel features between the two hemispheres, enhancing the sensitivity of the classification model to occluded vessels. To optimize the feature differential information among similar vessels, we further propose a novel cooperative learning loss function to minimize changes within classes and similarities between classes.Main results.We evaluate our proposed model on an intracranial LVO data set. Compared to state-of-the-art deep learning models, our model performs optimally, achieving a classification sensitivity of 93.73%, precision of 83.33%, accuracy of 89.91% and Macro-F1 score of 87.13%.Significance.This method can adaptively focus on occluded vessel regions and effectively train in scenarios with high inter-class similarity and intra-class variability, thereby improving the performance of LVO classification.

Enhancing the sensitivity and stability of electrochemical aptamer-based sensors by AuNPs@MXene nanocomposite for continuous monitoring of biomarkers.

Electrochemical aptamer-based (E-AB) sensors offer exciting potential for real-time tracking of various biomarkers, such as proteins and small molecules, due to their exceptional selectivity and adaptability. However, most E-AB sensors rely on planar gold structures, which inherently limit their sensitivity and operational stability for continuous monitoring of biomarkers. Although gold nanostructures have recently enhanced E-AB sensor performance, no studies have explored the combination of gold nanostructure with other types of nanomaterials for continuous molecular monitoring. To fill this gap, we employed gold nanoparticles and MXene Ti3C2 (AuNPs@MXene), a versatile nanocomposite, in designing an E-AB sensor targeted at vascular endothelial growth factor (VEGF), a crucial human signaling protein. Remarkably, the AuNPs@MXene nanocomposite achieved over thirty-fold and half-fold increases in active surface area compared to bare and AuNPs-modified gold electrodes, respectively, significantly elevating the analytical capabilities of E-AB sensors during continuous operation. After a systematic optimization and characterization process, the newly developed E-AB sensor, powered by AuNPs@MXene nanocomposite, demonstrated both enhanced stability and heightened sensitivity. Overall, our findings open new avenues for the incorporation of nanocomposites in E-AB sensor design, enabling the creation of more sensitive and durable real-time monitoring systems.

Construction of liquid metal-based soft microfluidic sensors via soft lithography.

BACKGROUND: Liquid metal (LM) can be integrated into microfluidic channel, bringing new functionalities of microfluidics and opening a new window for soft microfluidic electronics, due to the superior advantages of the conductivity and deformability of LMs. However, patterning the LMs into microfluidic channels requires either selective surface wetting or complex fabrication process. RESULTS: In this work, we develop a method to pattern the LMs onto the soft elastomer via soft lithographic process for fabrication of soft microfluidic sensors without the surface modification, bulky facilities, and complicated processes. The combination of the interfacial hydrogen bond and surface tension enables the LM patterns transfer to the soft elastomer. The transferred LM patterns with an ellipse-like cross-section further improve the stability under the mechanical deformation. Three proof-of-concept experiments were conducted to demonstrate the utilization of this method for development of thermochromic sensors, self-powered capacity sensors and flexible biosensor for glucose detection. CONCLUSIONS: In summary, the proposed method offers a new patterning method to obtain soft microfluidic sensors and brings new possibilities for microfluidics-related wearable devices.

Flexible Sensors for Hydrogen Peroxide Detection: A Critical Review.

Hydrogen peroxide (H2O2) is a common chemical used in many industries and can be found in various biological environments, water, and air. Yet, H2O2 in a certain range of concentrations can be hazardous and toxic. Therefore, it is crucial to determine its concentration at different conditions for safety and diagnostic purposes. This review provides an insight about different types of sensors that have been developed for detection of H2O2. Their flexibility, stability, cost, detection limit, manufacturing, and challenges in their applications have been compared. More specifically the advantages and disadvantages of various flexible substrates that have been utilized for the design of H2O2 sensors were discussed. These substrates include carbonaceous substrates (e.g., reduced graphene oxide films, carbon cloth, carbon, and graphene fibers), polymeric substrates, paper, thin glass, and silicon wafers. Many of these substrates are often decorated with nanostructures composed of Pt, Au, Ag, MnO2, Fe3O4, or a conductive polymer to enhance the performance of sensors. The impact of these nanostructures on the sensing performance of resulting flexible H2O2 sensors has been reviewed in detail. In summary, the detection limits of these sensors are within the range of 100 nM-1 mM, which makes them potentially, but not necessarily, suitable for applications in health, food, and environmental monitoring. However, the required sample volume, cost, ease of manufacturing, and stability are often neglected compared to other detection parameters, which hinders sensors' real-world application. Future perspectives on how to address some of the substrate limitations and examples of application-driven sensors are also discussed.