Journal of biophotonics最新文献

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.

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.

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.

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.

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.

The primary ocular effect of diabetes is diabetic retinopathy (DR), which is associated with diabetic microangiopathy. Diabetic macular edema (DME) can cause vision loss for people with DR. For this reason, deciding on the appropriate treatment and follow-up has a critical role in terms of curing the disease. Current artificial intelligence (AI) approaches focus on OCT images and may ignore clinical, laboratory, and demographic information obtained by the specialist. This study presents a novel deep learning (DL) framework for evaluating the visual outcome of the TREX anti-VEGF intravitreal injection regimen. DL models are trained to extract deep features from OCT and ILM topographic images and the obtained deep features are combined with patients' demographic, clinical, and laboratory findings to predict the direction of the treatment process. When the ResNet-18 network is used, the proposed DL framework is able to predict the prognosis status of patients with the highest accuracy.

Local therapeutic action and targeted drug release are promising approaches compared to traditional systemic drug administration. This is especially relevant for nitric oxide (NO), as its effects change dramatically depending on concentration and cellular context. Materials capable of releasing NO in deep tissues in a controlled manner might open new therapeutic opportunities. Light-sensitive NO donors represent a fascinating class of compounds with significant potential for precise and controlled NO release. However, most of them are sensitive to visible light, with only a few examples absorbing in a near-infrared therapeutic window. Here, we present the proof-of-concept of soft implants consisting of the photon upconverting core and the outer shell loaded with visible-light triggered NO donor. The separation into two compartments results in efficient energy harvesting by the dye and effective NO release under 980 nm infrared irradiation. Such implants could be used in smart therapies implying well-controlled and localized NO release.

We report on the development of a multimodal spectroscopy system, combining diffuse reflectance spectroscopy (DRS) and spatially offset Raman spectroscopy (SORS). A fiber optic probe was designed with spatially offset source-detector fibers to collect subsurface measurements for each modality, as well as ball lens-coupled fibers for superficial measurements. The system acquires DRS, zero-offset Raman spectroscopy (RS) and SORS with good signal-to-noise ratio. Measurements on chicken breast tissue demonstrate that both DRS and RS can acquire spectra from similar depths within tissue. Measurements acquired from the skin of a human volunteer demonstrate distinct Raman peaks at 937 and 1755 cm^-1 that were unique to the zero-offset ball lens configuration and 718 and 1089 cm^-1 for the spatially offset setting. We also identified Raman peaks corresponding to melanin that were prominent in the superficial measurements obtained with the ball lens-coupled fibers but not in the spatially offset fibers.

Breast augmentations, commonly performed for aesthetic or medical reasons, often use silicone (polydimethylsiloxane [PDMS]) implants. Some patients develop complications like capsular contracture, where scar tissue forms around the implant. Previously, we used stimulated Raman scattering (SRS) microscopy to detect and quantify silicone in stained capsule tissue, finding a correlation between silicone amount and contracture severity. However, we suspected silicone loss during histological preparation, which includes multiple steps like formalin fixation and paraffin embedding. In this study, we assessed silicone loss by comparing adjacent tissue samples from the same capsule: one prepared conventionally and the other snap-frozen. SRS microscopy revealed that snap-frozen samples had roughly five times more silicone, indicating significant silicone loss during conventional preparation. Thus, measuring silicone in histologically prepared samples likely underestimates PDMS content.

We report here on the development of tailored plasmonic AgNPs/C:H:N:O plasma polymer nanocomposites for the detection of the pathogenic bacterium Borrelia afzelii, with high selectivity and sensitivity. Silver (Ag) nanoparticles, generated by a gas aggregation source, are incorporated onto a C:H:N:O plasma polymer matrix, which is deposited by magnetron sputtering of a nylon 6.6. These anchored Ag nanoparticles propagate localized surface plasmon resonance (LSPR), optically responding to changes caused by immobilized pathogens near the nanoparticles. The tailored functionalization of AgNPs/C:H:N:O nanocomposite surface allows both high selectivity for the pathogen and high sensitivity with an LSPR red-shift Δλ > (4.20 ± 0.71) nm for 50 Borrelia per area 0.785 cm². The results confirmed the ability of LSPR modulation for the rapid and early detection of (not only) tested pathogens.