Reduced nicotinamide adenine dinucleotide (NADH) plays a crucial role in many biochemical reactions in human metabolism. In this work, a flow-mediated skin fluorescence (FMSF)-postocclusion reactive hyperaemia (PORH) system was developed for noninvasive and in vivo measurement of NADH fluorescence and its real-time dynamical changes in human skin tissue. The real-time dynamical changes of NADH fluorescence were analyzed with the changes of skin blood flow measured by laser speckle contrast imaging (LSCI) experiments simultaneously with FMSFPORH measurements, which suggests that the dynamical changes of NADH fluorescence would be mainly correlated with the intrinsic changes of NADH level in the skin tissue. In addition, Monte Carlo simulations were applied to understand the impact of optical property changes on the dynamical changes of NADH fluorescence during the PORH process, which further supports that the dynamical changes of NADH fluorescence measured in our system would be intrinsic changes of NADH level in the skin tissue.

Periodontitis is closely related to many systemic diseases linked by different periodontal pathogens. To unravel the relationship between periodontitis and systemic diseases, it is very important to correctly discriminate major periodontal pathogens. To realize convenient, e±cient, and high-accuracy bacterial species classification, the authors use Raman spectroscopy combined with machine learning algorithms to distinguish three major periodontal pathogens Porphyromonas gingivalis (Pg), Fusobacterium nucleatum (Fn), and Aggregatibacter actinomycetemcomitans (Aa). The result shows that this novel method can successfully discriminate the three abovementioned periodontal pathogens. Moreover, the classification accuracies for the three categories of the original data were 94.7% at the sample level and 93.9% at the spectrum level by the machine learning algorithm extra trees. This study provides a fast, simple, and accurate method which is very beneficial to differentiate periodontal pathogens.

Apoptosis is very important for the maintenance of cellular homeostasis and is closely related to the occurrence and treatment of many diseases. Mitochondria in cells play a crucial role in programmed cell death and redox processes. Nicotinamide adenine dinucleotide (NAD(P)H) is the primary producer of energy in mitochondria, changing NAD(P)H can directly reflect the physiological state of mitochondria. Therefore, NAD(P)H can be used to evaluate metabolic response. In this paper, we propose a noninvasive detection method that uses two-photon fluorescence lifetime imaging microscopy (TP-FLIM) to characterize apoptosis by observing the binding kinetics of cellular endogenous NAD(P)H. The result shows that the average fluorescence lifetime of NAD(P)H and the fluorescence lifetime of protein-bound NAD(P)H will be affected by the changing pH, serum content, and oxygen concentration in the cell culture environment, and by the treatment with reagents such as H2O2 and paclitaxel. Taxol (PTX). This noninvasive detection method realized the dynamic detection of cellular endogenous substances and the assessment of apoptosis.

Thermoacoustic imaging (TAI) is an emerging high-resolution and high-contrast imaging technology. In recent years, metal wires have been used in TAI experiments to quantitatively evaluate the spatial resolution of different systems. However, there is still a lack of analysis of the response characteristics and principles of metal wires in TAI. Through theoretical and simulation analyses, this paper proposes that the response of metal (copper) wires during TAI is equivalent to the response of antennas. More critically, the response of the copper wire is equivalent to the response of a half-wave dipole antenna. When its length is close to half the wavelength of the incident electromagnetic wave, it obtains the best response. In simulation, when the microwave excitation frequencies are 1.3 GHz, 3.0 GHz, and 5.3 GHz, and the lengths of copper wires are separately set to 11 cm, 5 cm, and 2.5 cm, the maximum SAR distribution and energy coupling e±ciency are obtained. This result is connected with the best response of half-wave dipole antennas with lengths of 11 cm, 4.77 cm, and 2.7 cm under the theoretical design, respectively. Regarding the further application, TAI can be used to conduct guided minimally invasive surgery on surgical instrument imaging. Thus, this paper indicated that results can also guide the design of metal surgical instruments utilized in different microwave frequencies.

A quantitative analysis method of CO2 laser treatments promotes laser treatment performance and rapid clinical application of novel treatment devices. The in silico clinical trial approach, which is based on computational simulation of light-tissue interactions using the mathematical model, can provide quantitative data. Although several simulation methods of laser tissue vaporization with CO2 laser treatments have been proposed, validations of the CO2 laser wavelength have been insu±cient. In this study, we demonstrated a tissue vaporization simulation using a CO2 laser and performed the experimental validation using a hydrogel phantom with constant physical parameters to evaluate the simulation accuracy of the vaporization process. The laser tissue vaporization simulation consists of the calculation of light transport, photothermal conversion, thermal diffusion, and phase change in the tissue. The vaporization width, depth, and area with CO2 laser irradiation to a tissue model were simulated. The simulated results differed from the actual vaporization width and depth by approximately 20% and 30%, respectively, because of the solubilization of the hydrogel phantom. Alternatively, the simulation vaporization area for all light irradiation parameters, which is related to the vaporization amount, agreed well with the actual vaporization values. These results suggest that the computational simulation can be used to evaluate the amount of tissue vaporization in the safety and effectiveness analysis of CO2 laser treatments.

X-ray-induced acoustic computed tomography (XACT) is a hybrid imaging modality for detecting X-ray absorption distribution via ultrasound emission. It facilitates imaging from a single projection X-ray illumination, thus reducing the radiation exposure and improving imaging speed. Nonuniform detector response caused by the interference between multichannel data acquisition for ring array transducers and amplifier systems yields ring artifacts in the reconstructed XACT images, which compromises the image quality. We propose model-based algorithms for ring artifacts corrected XACT imaging and demonstrate their e±cacy on numerical and experimental measurements. The corrected reconstructions indicate significantly reduced ring artifacts as compared to their conventional counterparts.

Brown adipose tissue (BAT) is a kind of adipose tissue engaging in thermoregulatory thermogenesis, metaboloregulatory thermogenesis, and secretory. Current studies have revealed that BAT activity is negatively correlated with adult body weight and is considered a target tissue for the treatment of obesity and other metabolic-related diseases. Additionally, the activity of BAT presents certain differences between different ages and genders. Clinically, BAT segmentation based on PET/CT data is a reliable method for brown fat research. However, most of the current BAT segmentation methods rely on the experience of doctors. In this paper, an improved U-net network, ICA-Unet, is proposed to achieve automatic and precise segmentation of BAT. First, the traditional 2D convolution layer in the encoder is replaced with a depth-wise overparameterized convolutional (Do-Conv) layer. Second, the channel attention block is introduced between the double-layer convolution. Finally, the image information entropy (IIE) block is added in the skip connections to strengthen the edge features. Furthermore, the performance of this method is evaluated on the dataset of PET/CT images from 368 patients. The results demonstrate a strong agreement between the automatic segmentation of BAT and manual annotation by experts. The average DICE coe±cient (DSC) is 0.9057, and the average Hausdorff distance is 7.2810. Experimental results suggest that the method proposed in this paper can achieve e±cient and accurate automatic BAT segmentation and satisfy the clinical requirements of BAT.

We propose a novel retinal layer segmentation method to accurately segment 10 retinal layers in optical coherence tomography (OCT) images with intraretinal fluid. The method used a fan filter to enhance the linear information pertaining to retinal boundaries in an OCT image by reducing the effect of vessel shadows and fluid regions. A random forest classifier was employed to predict the location of the boundaries. Two novel methods of boundary redirection (SR) and similarity correction (SC) were combined to carry out boundary tracking and thereby accurately locate retinal layer boundaries. Experiments were performed on healthy controls and subjects with diabetic macular edema (DME). The proposed method required an average of 415 s for healthy controls and of 482 s for subjects with DME and achieved high accuracy for both groups of subjects. The proposed method requires a shorter running time than previous methods and also provides high accuracy. Thus, the proposed method may be a better choice for small training datasets.

We investigated the relationship between chromophore concentrations in two-layered scattering media and the apparent chromophore concentrations measured with broadband optical spectroscopy in conjunction with commonly used homogeneous medium inverse models. We used diffusion theory to generate optical data from a two-layered distribution of relevant tissue absorbers, namely, oxyhemoglobin, deoxyhemoglobin, water, and lipids, with a top-layer thickness in the range 1–15 mm. The generated data consisted of broadband continuous-wave (CW) diffuse reflectance in the wavelength range 650–1024 nm, and frequency-domain (FD) diffuse reflectance at 690 and 830 nm; two source-detector distances of 25 and 35mm were used to simulate a dual-slope technique. The data were inverted using diffusion theory for a semi-infinite homogeneous medium to generate reduced scattering coe±cients at 690 and 830nm (from FD data) and effective absorption spectra in the range 650–1024nm (from CW data). The absorption spectra were then converted into effective total concentration and oxygen saturation of hemoglobin, as well as water and lipid concentrations. For absolute values, it was found that the effective hemoglobin parameters are typically representative of the bottom layer, whereas water and lipid represent some average of the respective concentrations in the two layers. For concentration changes, lipid showed a significant cross-talk with other absorber concentrations, thus indicating that lipid dynamics obtained in these conditions may not be reliable. These systematic simulations of broadband spectroscopy of two-layered media provide guidance on how to interpret effective optical properties measured with similar instrumental setups under the assumption of medium homogeneity.

The drug supervision methods based on near-infrared spectroscopy analysis are heavily dependent on the chemometrics model which characterizes the relationship between spectral data and drug categories. The preliminary application of convolution neural network in spectral analysis demonstrates excellent end-to-end prediction ability, but it is sensitive to the hyper-parameters of the network. The transformer is a deep-learning model based on self-attention mechanism that compares convolutional neural networks (CNNs) in predictive performance and has an easy-todesign model structure. Hence, a novel calibration model named SpectraTr, based on the transformer structure, is proposed and used for the qualitative analysis of drug spectrum. The experimental results of seven classes of drug and 18 classes of drug show that the proposed SpectraTr model can automatically extract features from a huge number of spectra, is not dependent on pre-processing algorithms, and is insensitive to model hyperparameters. When the ratio of the training set to test set is 8:2, the prediction accuracy of the SpectraTr model reaches 100% and 99.52%, respectively, which outperforms PLS DA, SVM, SAE, and CNN. The model is also tested on a public drug data set, and achieved classification accuracy of 96.97% without preprocessing algorithm, which is 34.85%, 28.28%, 5.05%, and 2.73% higher than PLS DA, SVM, SAE, and CNN, respectively. The research shows that the SpectraTr model performs exceptionally well in spectral analysis and is expected to be a novel deep calibration model after Autoencoder networks (AEs) and CNN.