An integrated microfluidic electrochemiluminescence device for point-of-care testing of acute myocardial infarction.

Heart-type fatty acid binding protein (H-FABP) is an early biomarker for acute myocardial infarction. The concentration of H-FABP in circulation sharply increases during myocardial injury. Therefore, fast and accurate detection of H-FABP is of vital significance. In this study, we developed an electrochemiluminescence device integrated with microfluidic chip (designed as m-ECL device) for on-site detection of H-FABP. The m-ECL device is consisted of a microfluidic chip that enable easy liquid handling as well as an integrated electronic system for voltage supply and photon detection. A sandwich-type ECL immunoassay strategy was employed for H-FABP detection by using Ru (bpy)32+ loaded mesoporous silica nanoparticles as ECL probes. This device can directly detect H-FABP in human serum without any pre-treatment, with a wide linear range of 1-100 ng/mL and a low limit of detection of 0.72 ng/mL. The clinical usability of this device was evaluated using clinical serum samples from patients. The results obtained from m-ECL device are well matched with those obtained from ELISA assays. We believe this m-ECL device has extensive application prospects for point-of-care testing of acute myocardial infarction.

Chemiresistive gas sensors based on electrospun semiconductor metal oxides: A review.

Semiconductor metal oxide (SMO) gas sensors have attracted considerable attention for detecting environmental pollution, as well as the accidental leakage of flammable, explosive, and toxic gases. SMOs are known to exhibit high sensitivity, fast response time, and excellent selectivity towards various types of gases. Many new strategies have been implemented to improve these characteristics. Among the materials produced by these methods, nanomaterials (NMs) synthesized by electrospinning have unprecedented advantages, including catalyst introduction, morphological control, thermodynamic stability, unique physicochemical properties, composition adjustment, and rapid adsorption-desorption rates of the NMs, and are appealing for the designing highly sensitive and selective gas sensors. This review highlights the latest findings on the design and fabrication of electrospun gas sensors for detecting various gases including hydrogen (H2), methane (CH4), nitrogen monoxide (NO), hydrogen sulfide (H2S), ammonia (NH3), ethanol (C2H5OH), acetone (CH3COCH3), formaldehyde (HCHO) and toluene (C6H5CH3). Studies have indicated that NMs with different shapes (e.g., nanotubes, nanowires, nanoflowers, nanosheets, nanorods, nanofilms, and nanofibers) and compositions (single-phase SMOs, modified SMOs, nanocomposites of SMOs, and SMOs combined with carbon nanomaterials) display high response values, long-term stability, low humidity dependence, fast response/recovery times, and low detection limits for gases. Finally, conclusions and future perspectives for gas sensors based on the electrospinning technique are discussed.

Artificial Intelligent Olfactory System for the Diagnosis of Parkinson's Disease.

Background: Currently, Parkinson's disease (PD) diagnosis is mainly based on medical history and physical examination, and there is no objective and consistent basis. By the time of diagnosis, the disease would have progressed to the middle and late stages. Pilot studies have shown that a unique smell was present in the skin sebum of PD patients. This increases the possibility of a noninvasive diagnosis of PD using an odor profile. Methods: Fast gas chromatography (GC) combined with a surface acoustic wave sensor with embedded machine learning (ML) algorithms was proposed to establish an artificial intelligent olfactory (AIO) system for the diagnosis of Parkinson's through smell. Sebum samples of 43 PD patients and 44 healthy controls (HCs) from Fourth Affiliated Hospital of Zhejiang University School of Medicine, China, were smelled by the AIO system. Univariate and multivariate methods were used to identify the significant volatile organic compound (VOC) features in the chromatograms. ML algorithms, including support vector machine, random forest (RF), k nearest neighbor (KNN), AdaBoost (AB), and Naive Bayes (NB), were used to distinguish PD patients from HC based on the VOC peaks in the chromatograms of sebum samples. Results: VOC peaks with average retention times of 5.7, 6.0, and 10.6 s, respectively, corresponding to octanal, hexyl acetate, and perillic aldehyde, were significantly different in PD and HC. The accuracy of the classification based on the significant features was 70.8%. Based on the odor profile, the classification had the highest accuracy and F1 of the five models with 0.855 from NB and 0.846 from AB, respectively, in the process of model establishing. The highest specificity and sensitivity of the five classifiers were 91.6% from NB and 91.7% from RF and KNN, respectively, in the evaluating set. Conclusions: The proposed AIO system can be used to diagnose PD through the odor profile of sebum. Using the AIO system is helpful for the screening and diagnosis of PD and is conducive to further tracking and frequent monitoring of the PD treatment process.