Single- or few-layer black phosphorus (FLBP) has attracted great attentions in scientific community with its excellent properties, including biodegradability, unique puckered lattice configuration, attractive electrical properties and direct and tunable band gap. In recent years, FLBP has been widely studied in bio-photonic fields such as photothermal and photodynamic therapy, drug delivery, bioimaging and biosensor, showing attractive clinical potential. Because of the marked advantages of FLBP nanomaterials in bio-photonic fields, this review article reviews the latest advances of biomaterials based on FLBP in biomedical applications, ranging from biocompatibility, medical diagnosis to treatment.

Traditional moxibustion therapy can stimulate heat and blood-vessel expansion and advance blood circulation. In the present study, a novel noncontact-type thermal therapeutic system was developed using a near-infrared laser diode. The device allows direct interaction of infrared laser light with the skin, thereby facilitating a controlled temperature distribution on the skin and the deep tissues below the skin. While using a tissue-mimicking phantom as a substitute for real skin, the most important optical and thermal parameters are the absorption/attenuation coe±cient, thermal conductivity, and specific heat. We found that these parameters can be manipulated by varying the agar-gel concentration. Hence, a multilayer tissue-mimicking phantom was fabricated using different agar-gel concentrations. Thermal imaging and thermocouples were used to measure the temperature distribution inside the phantom during laser irradiation. The temperature increased with the increase in the agar-gel concentration and reached a maximum value under the tissue phantom surface. To induce a similar thermal effect of moxibustion therapy, controlled laser-irradiation parameters such as output power, wavelength and pulse width were obtained from further analysis of the temperature distribution. From the known optothermal properties of the patient's skin, the temperature distribution inside the tissue was manipulated by optimizing the laser parameters. This study can contribute to patient-specific thermal therapy in clinics.

A technique for the determination of tannin content in traditional Chinese medicine injections (TCMI) was developed based on ultraviolet (UV) spectroscopy. Chemometrics were used to construct a mathematical model of absorption spectrum and tannin reference content of Danshen and Guanxinning injections, and the model was verified and applied. The results showed that the established UV-based spectral partial least squares regression (PLS) tannin content model performed well with a correlation coe±cient (r) of 0.952, root mean square error of calibration (RMSEC) of 0.476 μg/ml, root mean square error of validation (RMSEV) of 1.171 μg/ml, and root mean square error of prediction (RMSEP) of 0.465 μg/ml. Pattern recognition models using linear discriminant analysis (LDA) and k nearest neighbor (k-NN) classifiers based on UV spectrum could successfully classify different types of injections and different manufacturers. The established method to measure tannin content based on UV spectroscopy is simple, rapid and reliable and provides technical support for quality control of tannin in Chinese medicine injections.

Diabetes is a widespread and serious disease and noninvasive measurement has been in high demand. To address this problem, a power spectral density-based method was offered for determining glucose sensitive sub-bands in the nearinfrared (NIR) spectrum. The experiments were conducted using phantoms of different optical properties in-vitro conditions. The optical bands 1200–1300 nm and 2100–2200 nm were found feasible for measuring blood glucose. After that, a photoplethysmography (PPG)-based low cost and portable optical system was designed. It has six different NIR wavelength LEDs for illumination and an InGaAs photodiode for detection. Optical density values were calculated through the system and used as independent variables for multiple linear regression analysis. The results of blood glucose levels for 24 known healthy subjects showed that the optical system prediction was nearly 80% in the A zone and 20% in the B zone according to the Clarke Error Grid analysis. It was shown that a promising easyuse, continuous, and compact optical system had been designed.

We have recently introduced a new technique, coherent hemodynamics spectroscopy (CHS), which aims at characterizing a specific kind of tissue hemodynamics that feature a high level of covariation with a given physiological quantity. In this study, we carry out a detailed analysis of the significance of coherence and phase synchronization between oscillations of arterial blood pressure (ABP) and total hemoglobin concentration ([Hbt]), measured with near-infrared spectroscopy (NIRS) during a typical protocol for CHS, based on a cyclic thigh cuff occlusion and release. Even though CHS is based on a linear time invariant model between ABP (input) and NIRS measurands (outputs), for practical reasons in a typical CHS protocol, we induce finite “groups” of ABP oscillations, in which each group is characterized by a different frequency. For this reason, ABP (input) and NIRS measurands (output) are not stationary processes, and we have used wavelet coherence and phase synchronization index (PSI), as a metric of coherence and phase synchronization, respectively. PSI was calculated by using both the wavelet cross spectrum and the Hilbert transform. We have also used linear coherence (which requires stationary process) for comparison with wavelet coherence. Themethod of surrogate data is used to find critical values for the significance of covariation between ABP and [Hbt]. Because we have found similar critical values for wavelet coherence and PSI by using five of the most used methods of surrogate data, we propose to use the data-independent Gaussian random numbers (GRNs), for CHS. By using wavelet coherence and wavelet cross spectrum, and GRNs as surrogate data, we have found the same results for the significance of coherence and phase synchronization between ABP and [Hbt]: on a total set of 20 periods of cuff oscillations, we have found 17 coherent oscillations and 17 phase synchronous oscillations. Phase synchronization assessed with Hilbert transform yielded similar results with 14 phase synchronous oscillations. Linear coherence and wavelet coherence overall yielded similar number of significant values. We discuss possible reasons for this result. Despite the similarity of linear and wavelet coherence, we argue that wavelet coherence is preferable, especially if one wants to use baseline spontaneous oscillations, in which phase locking and coherence between signals might be only temporary.

In order to effectively improve the pathological diagnosis capability and feature resolution of 3D human brain CT images, a threshold segmentation method of multi-resolution 3D human brain CT image based on edge pixel grayscale feature decomposition is proposed in this paper. In this method, first, original 3D human brain image information is collected, and CT image filtering is performed to the collected information through the gradient value decomposition method, and edge contour features of the 3D human brain CT image are extracted. Then, the threshold segmentation method is adopted to segment the regional pixel feature block of the 3D human brain CT image to segment the image into block vectors with high-resolution feature points, and the 3D human brain CT image is reconstructed with the salient feature point as center. Simulation results show that the method proposed in this paper can provide accuracy up to 100% when the signal-to-noise ratio is 0, and with the increase of signal-to-noise ratio, the accuracy provided by this method is stable at 100%. Comparison results show that the threshold segmentation method of multi-resolution 3D human brain CT image based on edge pixel grayscale feature decomposition is significantly better than traditional methods in pathological feature estimation accuracy, and it effectively improves the rapid pathological diagnosis and positioning recognition abilities to CT images.

Noninvasive, glucose-monitoring technologies using infrared spectroscopy that have been studied typically require a calibration process that involves blood collection, which renders the methods somewhat invasive. We develop a truly noninvasive, glucose-monitoring technique using midinfrared spectroscopy that does not require blood collection for calibration by applying domain adaptation (DA) using deep neural networks to train a model that associates blood glucose concentration with mid-infrared spectral data without requiring a training dataset labeled with invasive blood sample measurements. For realizing DA, the distribution of unlabeled spectral data for calibration is considered through adversarial update during training networks for regression to blood glucose concentration. This calibration improved the correlation coe±cient between the true blood glucose concentrations and predicted blood glucose concentrations from 0.38 to 0.47. The result indicates that this calibration technique improves prediction accuracy for mid-infrared glucose measurements without any invasively acquired data.

Near-infrared spectroscopy (NIRS) can provide the hemodynamics information based on the hemoglobin concentration representing the blood oxygen metabolism of the cerebral cortical, which can be deployed for the cerebral function study. However, NIRS-based cerebral function detection accuracy can be significantly influenced by the physiological activities such as cardic cycle, respiration, spontaneous low-frequency oscillation and ultra-low frequency oscillation. The distribution difference of the capillary, artery and vein leads to the heterogeneity feature of the cerebral tissues. In the case that the heterogeneity is not serious, good detection accuracy and stable performance can be achieved through the regression analysis as the reference signal can well represent the interference in the measurement signal when conducting the multi-distance measurement approach. The direct use of the reference signal to estimate the interference is not able to achieve good performance in the case that the heterogeneity is serious. In this study, the cerebral function activity signal is extracted using recursive least square (RLS) method based on the multi-distance measurement method in which the reference signal is processed by ensemble empirical mode decomposition (EEMD) algorithm. The temporal and dimensional correlation of the neighboring sampling values are applied to estimate the interference in the measurement signal. Monte Carlo simulation based on a heterogeneous model is adopted here to investigate the effectiveness of this methodology. The results show that this methodology can effectively suppress the physiological interference and improve the detection accuracy of cerebral activity signal.