Journal of biophotonics最新文献

Bacterial identification is vital for healthcare and environmental quality control. Traditional bacterial identification methods require extensive sample preparation, including cultivation, staining, and microscopy, making them time-consuming and labor-intensive. This study proposes the application of Laser Speckle Contrast Imaging (LSCI) as a novel approach to capture variations in speckle patterns between the start and end of the lag caused by changes in the shape and arrangement of bacterial cells during cell division in liquid cultures at lower cell concentrations, such as in the lag phase. Our approach offers an efficient alternative to traditional methods of bacterial species identification demonstrated with Escherichia coli and Micrococcus luteus pairs. Also, the differentiation of strains (E. coli ATCC25922 and DH5α) is carried out based on the percentage change in speckle contrast between the end of lag and mid-log phase of their growth curve.

Photobiomodulation (PBM) is a noninvasive treatment modality that utilizes light to influence cellular activity. PBM has applications in various dermatological conditions. As blue light (BL, 400-500 nm) therapy gains popularity, concerns about its potential to induce DNA damage remain. This study investigates the effects of fluorescent BL (417 ± 5 nm) on human dermal fibroblast DNA, specifically examining the formation of cyclobutane pyrimidine dimers (CPD) and 6-4 photoproducts (6-4PPs) at fluences of 10, 30, and 45 J/cm². Our results demonstrate that BL exposure does not induce detectable DNA damage, in contrast to the well-documented effects of ultraviolet light, which is known to cause such damage. These findings support the safety of fluorescent BL therapy and align with prior research on LED-red and LED-near-infrared wavelengths. Continued clinical investigation of the effects of BL on skin will add to the understanding of the safety profile.

This study investigated the effects of combining repetitive transcranial magnetic stimulation (rTMS) with bilateral arm training (BAT) on effective brain connectivity in chronic stroke patients using functional near-infrared spectroscopy. Fifteen post-stroke patients and fifteen healthy individuals were enrolled. Coupling function analysis was performed to evaluate the effective connectivity inflow, outflow, and the dominant information flow (DIF) during standalone BAT and combined rTMS-BAT therapy. Significant task-related alterations were observed in the ipsilesional supplementary motor area and occipital lobe (OL) of stroke patients undergoing rTMS-BAT. During BAT, stroke patients exhibited more pronounced DIF from the OL to motor areas compared to healthy controls. Furthermore, in the rTMS-BAT condition, patients demonstrated enhanced DIF from the ipsilesional OL and contralesional prefrontal cortex to ipsilesional motor areas. These findings suggested a potential synergistic effect on cortical reorganization through the sequential combination of task-related training and TMS in chronic stroke patients, offering insights into rehabilitation strategies.

Chronic alcohol consumption significantly impacts physiological and neurological functions. This study aimed to investigate the biochemical alterations in serum associated with long-term alcohol use using Fourier Transform Infrared (FTIR) spectroscopy. Serum samples from control and alcohol use disorder (AUD) were analyzed, and their spectra were compared. Multivariate analysis techniques, including Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA), were employed to differentiate the groups. A machine learning model, Grid Search-Support Vector Machine Discriminant Analysis (GS-SVMDA), was developed to classify samples with high accuracy. Significant differences in the absorbance values of specific functional groups, particularly those associated with phospholipids, amides, and fatty acids revealed. The AUD exhibited lower levels of these biomolecules. The models achieved perfect classification, demonstrating the potential ofFTIR spectroscopy as a non-invasive tool for diagnosing AUD. Findings contribute to a better understanding of the biochemical mechanisms underlying alcohol addiction and may aid in the development of novel diagnostic and therapeutic strategies.

The incidence of male infertility has kept increasing year by year, severely affecting the sustainability of society. Sperm morphological analysis plays an important role in assessing sperm fertility. However, current sperm morphological analysis is usually performed using microscopic images of stained sperm, which requires complex staining operations, making it difficult to achieve a comprehensive and efficient diagnosis. In this work, we propose and experimentally demonstrate sperm morphological analysis with optofluidic time-stretch (OTS) imaging flow cytometry. Specifically, we capture the stain-free sperm images with high-throughput OTS imaging flow cytometry and analyze the morphological features using a U-Net network. The analysis of 40,000 images from 20 clinical semen samples indicates that our method achieves higher accuracy than the smear test and shows high concordance with the results using comprehensive methods of routine semen examination. This work provides a high-throughput, stain-free sperm morphological analysis method, which holds promise for a comprehensive evaluation of semen.

In this paper, we present a noninvasive method for the accurate estimation of methemoglobin concentration. The proposed technique incorporates a novel machine learning model using the artificial neural network to detect methemoglobin and oxygen saturation from the diffuse reflectance spectra of skin tissue. Sixty-six spectra were simulated using a four-layer tissue model with varying oxygen saturation and methemoglobin concentration. A multifiber probe-based DRS setup in the visible and near-infrared wavelength range was used. The best accuracy, with a mean absolute error (MAE) of 0.0392% for the concentration of methemoglobin and 0.0273% for the percentage of oxygen saturation on the created data set, was achieved. Our method was also experimentally verified using DRS spectra collected from human subjects. Consequently, the findings demonstrate the ability of broadband DRS to noninvasively differentiate subtle changes in methemoglobin and hemoglobin levels despite their overlapping spectral features.

Uterine fibroids are non-cancerous growths of the uterus that affect nearly 70%-80% of women in their lifetimes. Fibroids can cause severe pain, bleeding, and infertility. The main risk of recurrence is smaller fibroids, which are notoriously hard to detect, being missed during a surgical removal procedure, only to enlarge afterwards. In this work, hyperspectral imaging (HSI) datasets were acquired from samples from 10 patients after receiving a hysterectomy. Optical properties including absorption, scattering, and spectral morphology were extracted and fed into machine learning to classify regions as fibroid and myometrium. Top extracted optical features had significant contrast between fibroid and myometrium (p < 0.0001) and were used to train Random Forest (AUC: 0.9985 ± 0.001, Sensitivity: 0.9534 ± 0.019, Specificity: 0.9936 ± 0.009) and Logistic Regression (AUC: 0.9397 ± 0.013, Sensitivity: 0.8405 ± 0.023, Specificity: 0.8895 ± 0.032) with strong performance across testing splits. With HSI, there is contrast between fibroid and myometrium in the human uterus.

In conventional microscopic imaging of chromosomes, the use of high numerical aperture (NA) oil-immersion objectives is essential. However, the use of oil-immersion objectives poses a significant challenge to automated imaging systems because it increases the risk of sample contamination and instrumental damage. The shallow depth of field (DOF) of oil-immersion objectives also demands more sophisticated mechanical focusing. Here, we introduce a chromosome oil-free microscopic imaging system based on Fourier Ptychographic Microscopy (FPM) technology. The system employs a 100×, 0.8 NA dry objective to achieve a half-pitch resolution of 194 nm at an incident wavelength of 524 nm. The reconstructed images of chromosomes surpassed the conventional imaging with a 100×, 1.25 NA oil-immersion objective. We also utilized digital refocusing methods to extend the effective DOF to ±2.4 μm. This study preliminarily validates the possibility of developing a new generation of chromosome scanners without using an oil-immersion objective.

Light-sheet fluorescence microscopy (LSFM) provides an ideal tool for long-term observation of live specimens due to its low photodamage and fast volumetric imaging speed. The wavefront distortions in the illumination path of LSFM will reduce the intensity and broaden the light-sheet thickness, thereby degrading the image quality. We propose to use the wavefront shaping technique to reduce the scattering effect and shrink the light-sheet thickness. Scanning the refocused laser beam to generate LS improves both the fluorescence intensity and the axial resolution. The axial resolution can be further enhanced by subtracting the two images captured via double scanning the samples with the refocused beam and the uncorrected scattered beam for each slice. The axial resolution is improved from 2.2 ± 0.3 to 1.5 ± 0.2 μm across the field of view of 270 μm × 270 μm. The effectiveness of the wavefront shaping subtraction method is demonstrated by imaging fluorescent beads and Aspergillus conidiophores behind a scattering medium.

Near-infrared (NIR) spectroscopy, known for its non-destructive, rapid, and precise nature, captures spectral responses to chemical bond changes in cancerous tissues. This provides a promising approach for accurate cancer staging and identifying spectral differences between cancerous and healthy tissues. In this study, NIR data from esophageal lesions excised via endoscopic submucosal dissection were analyzed using partial least squares discriminant analysis (PLS-DA) to classify normal tissues, low-grade, and high-grade intraepithelial neoplasia, confirming its feasibility for staging diagnosis. To enhance wavelength selection, the FOX algorithm, a swarm intelligence optimization method, is improved with two modifications: a nonlinear time-varying sigmoid transfer function and mirror selection. These enhancements are combined to form an improved FOX algorithm (iFOX) for wavelength selection. iFOX effectively enhances the algorithm's stability while enhancing classification performance.