A Self-Powered Lactate Sensor Based on the Piezoelectric Effect for Assessing Tumor Development.

The build-up of lactate in solid tumors stands as a crucial and early occurrence in malignancy development, and the concentration of lactate in the tumor microenvironment may be a more sensitive indicator for analyzing primary tumors. In this study, we designed a self-powered lactate sensor for the rapid analysis of tumor samples, utilizing the coupling between the piezoelectric effect and enzymatic reaction. This lactate sensor is fabricated using a ZnO nanowire array modified with lactate oxidase (LOx). The sensing process does not require an external power source or batteries. The device can directly output electric signals containing lactate concentration information when subjected to external forces. The lactate concentration detection upper limit of the sensor is at least 27 mM, with a limit of detection (LOD) of approximately 1.3 mM and a response time of around 10 s. This study innovatively applied self-powered technology to the in situ detection of the tumor microenvironment and used the results to estimate the growth period of the primary tumor. The availability of this application has been confirmed through biological experiments. Furthermore, the sensor data generated by the device offer valuable insights for evaluating the likelihood of remote tumor metastasis. This study may expand the research scope of self-powered technology in the field of medical diagnosis and offer a novel perspective on cancer diagnosis.

Facial age recognition based on deep manifold learning.

Facial age recognition has been widely used in real-world applications. Most of current facial age recognition methods use deep learning to extract facial features to identify age. However, due to the high dimension features of faces, deep learning methods might extract a lot of redundant features, which is not beneficial for facial age recognition. To improve facial age recognition effectively, this paper proposed the deep manifold learning (DML), a combination of deep learning and manifold learning. In DML, deep learning was used to extract high-dimensional facial features, and manifold learning selected age-related features from these high-dimensional facial features for facial age recognition. Finally, we validated the DML on Multivariate Observations of Reactions and Physical Health (MORPH) and Face and Gesture Recognition Network (FG-NET) datasets. The results indicated that the mean absolute error (MAE) of MORPH is 1.60 and that of FG-NET is 2.48. Moreover, compared with the state of the art facial age recognition methods, the accuracy of DML has been greatly improved.

Validation of a new non-hyperemic physiological index: the constant-resistance ratio (cRR).

OBJECTIVES: The instantaneous wave-free ratio (iwFR) has limited availability. A new resting index called the constant-resistance ratio (cRR), which dynamically identifies cardiac intervals with constant and minimum resistance, has been developed; however, its diagnostic performance is unknown. The aim of this study was to validate the cRR by retrospectively calculating the cRR values from raw pressure waveforms of 2 publicly available datasets and compare them with those of the iwFR. METHODS: Waveform data from the CONTRAST and VERIFY 2 studies were used. The primary endpoint was Bland-Altman bias between cRR and iwFR. Secondary endpoints included diagnostic agreement, correlation, receiver operating characteristic (ROC) analysis, and success rates of cRR and iwFR. RESULTS: Among the 1036 waveforms, 871 were successful in determining paired cRR and iwFR values, while cRR was 6% more successful than iwFR (P less than .0001). The mean bias between cRR and iwFR was 0.003, with 95% limits of agreement [-0.021,0.028]. These 2 indices were highly correlated (r = 0.991; P less than .0001). Using an iwFR of 0.89 or less as the reference standard, the optimal cRR cutoff was 0.89, with an area under the ROC curve of 0.991 (P less than .001) and a diagnostic accuracy of 96.9% (95% CI [96%, 98%]). CONCLUSIONS: The cRR, a new resting index for identifying dynamic cardiac intervals with constant and minimum resistance, demonstrated high numerical agreement, diagnostic consistency, and a higher success rate than the iwFR based on the 2 publicly available datasets.