Journal of biophotonics最新文献

Prostate-specific antigen (PSA) is the most commonly used marker of prostate cancer. However, nearly 25% of men with elevated PSA levels do not have cancer and nearly 20% of patients with prostate cancer have normal serum PSA levels. Therefore, in this study, Fourier transform infrared (FTIR) spectroscopy was investigated as a new tool for detection of prostate cancer from urine. Obtained results showed higher levels of glucose, urea and creatinine in urine collected from patients with prostate cancer than that in control. Principal component analysis (PCA) was not noticed possibility of differentiation urine collected from healthy and nonhealthy patients. However, machine learning algorithms showed 0.90 accuracy and precision of FTIR in detection of prostate cancer from urine. We showed that wavenumbers at 1614 cm^-1 and 2972 cm^-1 were candidates for prostate cancer spectroscopy markers. Importantly, these FTIR markers correlated with Gleason score, PSA and mpMRI PI-RADS category.

Transcranial ultrasound imaging is a popular method to study cerebral functionality and diagnose brain injuries. However, the detected ultrasound signal is greatly distorted due to the aberration caused by the skull bone. The aberration mechanism mainly depends on thickness and porosity, two important skull physical characteristics. Although skull bone thickness and porosity can be estimated from CT or MRI scans, there is significant value in developing methods for obtaining thickness and porosity information from ultrasound itself. Here, we extracted various features from ultrasound signals using physical skull-mimicking phantoms of a range of thicknesses with embedded porosity-mimicking acoustic mismatches and analyzed them using machine learning (ML) and deep learning (DL) models. The performance evaluation demonstrated that both ML- and DL-trained models could predict the physical characteristics of a variety of skull phantoms with reasonable accuracy. The proposed approach could be expanded upon and utilized for the development of effective skull aberration correction methods.

The aim of this study was to detect biochemical components in the urine of bodybuilders who ingested creatine pretraining compared to individuals who did not ingest creatine after physical exercise using Raman spectroscopy. Twenty volunteers practicing bodybuilding were selected to collect pre- and post-training urine samples, where 10 volunteers ingested creatine 30 min before pretraining urine collection (creatine group), and 10 did not (control group). The samples were subjected to Raman spectroscopy, and the spectra of both creatine and control groups and the difference (post-pre) for both groups were analyzed. Principal component analysis (PCA) technique was applied to the samples. The results showed peaks of creatine and phosphate in urine after training (creatine post-training group), suggesting that part of the creatine was absorbed and metabolized, and part was excreted. Raman spectroscopy could be applied to detect biocompounds in urine, such as unmetabolized creatine and phosphate.

The image contrast and probing depth of optical methods applied to in vivo skin could be improved by reducing skin scattering using the optical clearing method. The aim of this study was to quantify, from line-field confocal optical coherence tomography (LC-OCT) 3D images, the modifications of skin scattering properties in vivo during optical clearing. Nine mixtures of optical clearing agents were used in combination with physical and chemical permeation enhancers on the human skin of three healthy volunteers. Scattering coefficient and anisotropy factor of the epidermis and the upper dermis were estimated from the 3D LC-OCT images of skin using an exponential decay model of the in-depth intensity profile. We were able to demonstrate a decrease in epidermal scattering (down to 33%) related to optical clearing, with the best results obtained by a mixture of polyethylene glycol, oleic acid, and propylene glycol.

We aim to evaluate the feasibility of Raman spectroscopy for parathyroid gland (PG) identification during thyroidectomy. Using a novel side-viewing handheld Raman probe, a total of 324 Raman spectra of four tissue types (i.e., thyroid, lymph node, PG, and lipid) commonly encountered during thyroidectomy were rapidly (< 3 s) acquired from 80 tissue sites (thyroid [n = 10], lymph node [n = 10], PG [n = 40], lipid [n = 20]) of 10 euthanized Wistar rats. Two partial least-squares (PLS)-discriminant analysis (DA) detection models were developed, differentiating the lipid and nonlipid (i.e., thyroid, lymph node, and PG) tissues with an accuracy of 100%, and PG, lymph node, and thyroid could be detected with an accuracy of 98.4%, 93.9%, and 95.4% respectively. This work demonstrates the feasibility of Raman spectroscopy technique for PG identification and protection during thyroidectomy at the molecular level.

Automatic segmentation of blood vessels in fundus images is important to assist ophthalmologists in diagnosis. However, automatic segmentation for Optical Coherence Tomography Angiography (OCTA) blood vessels has not been fully investigated due to various difficulties, such as vessel complexity. In addition, there are only a few publicly available OCTA image data sets for training and validating segmentation algorithms. To address these issues, we constructed a wild-field retinal OCTA segmentation data set, the Retinal Vessels Images in OCTA (REVIO) dataset. Second, we propose a new retinal vessel segmentation network based on spatial and frequency domain networks (SFNet). The proposed model are tested on three benchmark data sets including REVIO, ROSE and OCTA-500. The experimental results show superior performance on segmentation tasks compared to the representative methods.

Necrotizing enterocolitis (NEC) is a devastating disease affecting premature infants. Broadband optical spectroscopy (BOS) is a method of noninvasive optical data collection from intra-abdominal organs in premature infants, offering potential for disease detection. Herein, a novel machine learning approach, iterative principal component analysis (iPCA), is developed to select optimal wavelengths from BOS data collected in vivo from neonatal intensive care unit (NICU) patients for NEC classification. Neural network models were trained for classification, with a reduced-feature model distinguishing NEC with an accuracy of 88%, a sensitivity of 89%, and a specificity of 88%. While whole-spectrum models performed the best for accuracy and specificity, a reduced feature model excelled in sensitivity, with minimal cost to other metrics. This research supports the hypothesis that the analysis of human tissue via BOS may permit noninvasive disease detection. Furthermore, a medical device optimized with these models may potentially screen for NEC with as few as seven wavelengths.

Phyllodes tumors (PTs) are rare breast stroma neoplasms, and their accurate identification at various stages is essential for personalized patient treatment. In this study, multiphoton microscopy (MPM) with two-photon excited fluorescence (TPEF) and second harmonic generation (SHG) imaging was used for label-free detection and differentiation of PTs and normal breast tissue. An automated image processing strategy was developed to quantify changes in collagen fiber morphology within the stroma and boundary of PTs, establishing optical diagnostic characteristics of PTs using MPM. The results demonstrated that MPM could be used for the detection of different stages of PTs, and the morphological alterations in collagen fibers could serve as critical indicators of PT malignancy, offering new insights for the diagnosis and grading of benign, borderline, and malignant PTs. It lays the groundwork for the future application of compact MPM for the rapid detection and diagnosis of PTs.

Research has not demonstrated whether multiple cups of negative pressure cupping therapy would induce interactions of hemodynamic responses between different areas. A multichannel near-infrared spectroscopy (NIRS) was used to assess oxyhemoglobin and deoxyhemoglobin oscillations in response to cupping therapy. Wavelet transform and wavelet phase (WPC) coherence were used to quantify NIRS signals. Three levels of negative pressure (-75, -225, and -300 mmHg) were applied to the gastrocnemius in 12 healthy adults. Oxyhemoglobin coherence between the two inside-cup areas was higher at -75 mmHg compared to -300 mmHg in both metabolic (WPC = 0.80 ± 0.11 vs. 0.73 ± 0.13) and neurogenic (WPC = 0.70 ± 0.11 vs. 0.60 ± 0.17) controls. Deoxyhemoglobin coherence was also higher at -75 mmHg compared to -300 mmHg in both metabolic (WPC = 0.78 ± 0.11 vs. 0.66 ± 0.14) and neurogenic (WPC = 0.67 ± 0.11 vs. 0.58 ± 0.13) controls. Our study provides first evidence on the interaction of hemodynamic responses between the two cups of cupping therapy using WPC analysis of NIRS signals.

Laser-irradiation-assisted cell gene transfection is sterile and nontoxic, but the low transfection efficiency cannot meet the application requirements. To improve the efficiency, a temporal and spatial shaping method of a femtosecond laser is proposed. Using the time shaping method, we can segment the pulse into subpulses of varying energies and with a defined delay, thereby influencing the interaction between electrons and photons, ultimately enhancing transfection efficiency. The transfection efficiency is further improved by spatially shaping the laser pulse to extend the focusing beam's working distance and reduce the cell's sensitivity to the focal position. Through the characterization of the viability and transfection efficiency of HEK-293T cells, the method achieved efficient and active transfection, with a maximum transfection efficiency of 45.1% and a cell survival rate of 93.6%. This method provides key technical support for femtosecond laser transfection and promotes its further application in clinical practice.