Enhancing resolution and contrast in fibre bundle-based fluorescence microscopy using generative adversarial network.

Fibre bundle (FB)-based endoscopes are indispensable in biology and medical science due to their minimally invasive nature. However, resolution and contrast for fluorescence imaging are limited due to characteristic features of the FBs, such as low numerical aperture (NA) and individual fibre core sizes. In this study, we improved the resolution and contrast of sample fluorescence images acquired using in-house fabricated high-NA FBs by utilising generative adversarial networks (GANs). In order to train our deep learning model, we built an FB-based multifocal structured illumination microscope (MSIM) based on a digital micromirror device (DMD) which improves the resolution and the contrast substantially compared to basic FB-based fluorescence microscopes. After network training, the GAN model, employing image-to-image translation techniques, effectively transformed wide-field images into high-resolution MSIM images without the need for any additional optical hardware. The results demonstrated that GAN-generated outputs significantly enhanced both contrast and resolution compared to the original wide-field images. These findings highlight the potential of GAN-based models trained using MSIM data to enhance resolution and contrast in wide-field imaging for fibre bundle-based fluorescence microscopy. Lay Description: Fibre bundle (FB) endoscopes are essential in biology and medicine but suffer from limited resolution and contrast for fluorescence imaging. Here we improved these limitations using high-NA FBs and generative adversarial networks (GANs). We trained a GAN model with data from an FB-based multifocal structured illumination microscope (MSIM) to enhance resolution and contrast without additional optical hardware. Results showed significant enhancement in contrast and resolution, showcasing the potential of GAN-based models for fibre bundle-based fluorescence microscopy.

An adaptive feature extraction method for classification of Covid-19 X-ray images.

This study aims to detect Covid-19 disease in the fastest and most accurate way from X-ray images by developing a new feature extraction method and deep learning model . Partitioned Tridiagonal Enhanced Multivariance Products Representation (PTMEMPR) method is proposed as a new feature extraction method by using matrix partition in TMEMPR method which is known as matrix decomposition method in the literature. The proposed method which provides 99.9% data reduction is used as a preprocessing method in the scheme of the Covid-19 diagnosis. To evaluate the performance of the proposed method, it is compared with the state-of-the-art feature extraction methods which are Singular Value Decomposition(SVD), Discrete Wavelet Transform(DWT) and Discrete Cosine Transform(DCT). Also new deep learning models which are called FSMCov, FSMCov-N and FSMCov-L are developed in this study. The experimental results indicate that the combination of newly proposed feature extraction method and deep learning models yield an overall accuracy 99.8%.

DeepCAN: A Modular Deep Learning System for Automated Cell Counting and Viability Analysis.

Precise and quick monitoring of key cytometric features such as cell count, size, morphology, and DNA content is crucial in life science applications. Traditionally, image cytometry relies on visual inspection of hemocytometers. This approach is error-prone due to operator subjectivity. Recently, deep learning approaches have emerged as powerful tools enabling quick and accurate image cytometry applicable to different cell types. Leading to simpler, compact, and affordable solutions, these approaches revealed image cytometry as a viable alternative to flow cytometry or Coulter counting. In this study, we demonstrate a modular deep learning system, DeepCAN, providing a complete solution for automated cell counting and viability analysis. DeepCAN employs three different neural network blocks called Parallel Segmenter, Cluster CNN, and Viability CNN that are trained for initial segmentation, cluster separation, and viability analysis. Parallel Segmenter and Cluster CNN blocks achieve accurate segmentation of individual cells while Viability CNN block performs viability classification. A modified U-Net network, a well-known deep neural network model for bioimage analysis, is used in Parallel Segmenter while LeNet-5 architecture and its modified version Opto-Net are used for Cluster CNN and Viability CNN, respectively. We train the Parallel Segmenter using 15 images of A2780 cells and 5 images of yeasts cells, containing, in total, 14742 individual cell images. Similarly, 6101 and 5900 A2780 cell images are employed for training Cluster CNN and Viability CNN models, respectively. 2514 individual A2780 cell images are used to test the overall segmentation performance of Parallel Segmenter combined with Cluster CNN, revealing high Precision/Recall/F1-Score values of 96.52%/96.45%/98.06%, respectively. Cell counting/viability performance of DeepCAN is tested with A2780 (2514 cells), A549 (601 cells), Colo (356 cells), and MDA-MB-231 (887 cells) cell images revealing high analysis accuracies of 96.76%/99.02%, 93.82%/95.93%, and 92.18%/97.90%, 85.32%/97.40%, respectively.

Protective effect of colchicine on ovarian ischemia-reperfusion injury: an experimental study.

OBJECTIVE: The aim of the present study is to investigate the efficiency of colchicine in the experimental rat ovarian torsion model in the light of histological and biochemical data. STUDY DESIGN: A total of 35 Wistar albino female rats were randomly divided into 5 groups, group 1: (control-sham operated, n = 7); group 2: (torsion/detorsion, n = 7) 2 hours of ischemia and 2 hours of reperfusion; group 3: (torsion/detorsion, n = 7), 2 hours of ischemia and 5 days of reperfusion; group 4: (torsion/detorsion, n = 7) 2 hours of ischemia and 2 hours of reperfusion and a signal dose of oral 1 mL/kg colchicine; and group 5: (torsion/detorsion, n = 7), 2 hours of ischemia and 5 days of reperfusion and 5 days of oral 1 mg/kg colchicine. Histopathologic evaluation was performed by a scoring that assesses congestion, bleeding, edema, and cellular degeneration in the ovarian tissue. Catalase, tissue malondialdehyde (MDA), and protein carbonyl levels were calculated. RESULTS: The histopathologic scores, MDA, and protein carbonyl levels in the control and colchicine groups were significantly lower than groups 2 and 3 (P < .001). Catalase activities were significantly higher in the control and colchicine groups than in groups 2 and 3 (P < .001). The results of the histopathologic parameters and biochemical markers showed that protective effects of colchicine treatment persisted up to 5 days. CONCLUSION: Our study results revealed that colchicine reduced ovarian ischemia-reperfusion injury in experimental rat ovarian torsion model. As the ovarian detorsion is the first choice of the treatment modality in the early phase, antioxidant and anti-inflammatory treatment modalities like colchicine might be used to reduce ovarian ischemia-reperfusion injury.

Zofenopril attenuates injury induced by ischemia-reperfusion on rat ovary.

AIM: The aim of the study was to investigate the effectiveness of zofenopril in an experimental model of ovarian torsion in rats with histologic and biochemical assessments. MATERIAL AND METHODS: Experimental procedures were performed on 35 female rats (Wistar albino). Rats were randomly divided into five groups as: sham (sham operated, n = 7); vehicle group 1 (torsion-detorsion, n = 7) with 2 h ischemia and 2 h reperfusion; vehicle group 2 (torsion-detorsion, n = 7) with 2 h ischemia and 5 days' reperfusion; zofenopril group 1 (torsion-detorsion, n = 7) with 2 h ischemia, 2 h reperfusion and a signal dose of oral 15 mg/kg zofenopril; and zofenopril group 2 (torsion-detorsion, n = 7) with 2 h ischemia, 5 days' reperfusion and 5 days' oral 15 mg/kg zofenopril. A scoring of histopathologic evaluation was performed on the ovaries according to congestion, bleeding, edema, and cellular degeneration. Biochemical assessments included catalase, tissue malondialdehyde and protein carbonyl. RESULTS: Compared with the vehicle groups, histopathologic scores, tissue malondialdehyde and protein carbonyl levels, which reflect oxidative stress markers, were significantly lower in the zofenopril groups. Furthermore, catalase levels were significantly increased in the zofenopril group. CONCLUSION: Our study results revealed that zofenopril attenuates injury induced by ischemia-reperfusion on rat ovary.