Maxillary sinus detection on cone beam computed tomography images using ResNet and Swin Transformer-based UNet.

OBJECTIVES: This study, which uses artificial intelligence-based methods, aims to determine the limits of pathologic conditions and infections related to the maxillary sinus in cone beam computed tomography (CBCT) images to facilitate the work of dentists. METHODS: A new UNet architecture based on a state-of-the-art Swin transformer called Res-Swin-UNet was developed to detect sinus. The encoder part of the proposed network model consists of a pre-trained ResNet architecture, and the decoder part consists of Swin transformer blocks. Swin transformers achieve powerful global context properties with self-attention mechanisms. Because the output of the Swin transformer generates sectorized features, the patch expanding layer was used in this section instead of the traditional upsampling layer. In the last layer of the decoder, sinus diagnosis was conducted through classical convolution and sigmoid function. In experimental works, we used a data set including 298 CBCT images. RESULTS: The Res-Swin-UNet model achieved more success, with a 91.72% F1-score, 99% accuracy, and 84.71% IoU, than outperforming the state-of-the-art models. CONCLUSIONS: The deep learning-based model proposed in the present study can assist dentists in automatically detecting the boundaries of pathologic conditions and infections within the maxillary sinus based on CBCT images.

Phytochemical profile, antioxidant, antiproliferative, and enzyme inhibition-docking analyses of Salvia ekimiana Celep & Dogan.

Salvia ekimiana Celep & Dogan is an endemic species of Turkey. To our knowledge, the number of studies on biological activities and phytochemical profiling of this plant is quite limited. Therefore, this study aimed to analyze its activities and phytochemical content in detail. The qualitative-quantitative compositions were determined via spectrophotometric and chromatographic (LC-MS/MS and HPLC) techniques. 1,1-Diphenyl-2-picrylhydrazyl radical (DPPH•) and 2,2'-Azino-bis 3-ethylbenzothiazoline-6-sulfonic acid (ABTS•+) radical scavenging and ascorbate-iron (III)-catalyzed phospholipid peroxidation experiments were performed to measure antioxidant capacity. Hyaluronidase, collagenase, and elastase enzyme inhibition tests were determined in vitro using a spectrophotometer. Antiproliferative activity was evaluated in human lung cancer (A549) and human breast cancer (MCF7) cells. The murine fibroblast (L929) cell line was used as a normal control cell. While the subextract rich in phenolic compounds was n-butanol extract, rosmarinic acid was defined as the main secondary metabolite. The highest antioxidant activity observed for the n-butanol subextract included the following: DPPH• EC50: 0.08±0.00 mg/mL, TEAC/ABTS: 2.19±0.09 mmol/L Trolox, MDA EC50: 0.42±0.03 mg/mL. The methanolic extract, the ethyl acetate, and n-butanol subextracts displayed significant inhibitory activity on collagenase, while the other subextracts did not show any inhibitory activity on hyaluronidase and elastase. Due to strong interactions with their active sites, molecular docking showed luteolin 7-glucuronide, apigenin 7-glucuronide, and luteolin 5-glucoside had the highest binding affinity with target enzymes. The chloroform subextract showed significant cytotoxicity in all cell lines. These novel results revealed that S. ekimiana has strong antioxidant, collagenase enzyme inhibitory, and cytotoxic potential.

Efficient COVID-19 Segmentation from CT Slices Exploiting Semantic Segmentation with Integrated Attention Mechanism.

Coronavirus (COVID-19) is a pandemic, which caused suddenly unexplained pneumonia cases and caused a devastating effect on global public health. Computerized tomography (CT) is one of the most effective tools for COVID-19 screening. Since some specific patterns such as bilateral, peripheral, and basal predominant ground-glass opacity, multifocal patchy consolidation, crazy-paving pattern with a peripheral distribution can be observed in CT images and these patterns have been declared as the findings of COVID-19 infection. For patient monitoring, diagnosis and segmentation of COVID-19, which spreads into the lung, expeditiously and accurately from CT, will provide vital information about the stage of the disease. In this work, we proposed a SegNet-based network using the attention gate (AG) mechanism for the automatic segmentation of COVID-19 regions in CT images. AGs can be easily integrated into standard convolutional neural network (CNN) architectures with a minimum computing load as well as increasing model precision and predictive accuracy. Besides, the success of the proposed network has been evaluated based on dice, Tversky, and focal Tversky loss functions to deal with low sensitivity arising from the small lesions. The experiments were carried out using a fivefold cross-validation technique on a COVID-19 CT segmentation database containing 473 CT images. The obtained sensitivity, specificity, and dice scores were reported as 92.73%, 99.51%, and 89.61%, respectively. The superiority of the proposed method has been highlighted by comparing with the results reported in previous studies and it is thought that it will be an auxiliary tool that accurately detects automatic COVID-19 regions from CT images.

DCCMED-Net: Densely connected and concatenated multi Encoder-Decoder CNNs for retinal vessel extraction from fundus images.

Recent studies have shown that convolutional neural networks (CNNs) can be more accurate, efficient and even deeper on their training if they include direct connections from the layers close to the input to those close to the output in order to transfer activation maps. Through this observation, this study introduces a new CNN model, namely Densely Connected and Concatenated Multi Encoder-Decoder (DCCMED) network. DCCMED contains concatenated multi encoder-decoder CNNs and connects certain layers to the corresponding input of the subsequent encoder-decoder block in a feed-forward fashion, for retinal vessel extraction from fundus image. The DCCMED model has assertive aspects such as reducing pixel-vanishing and encouraging features reuse. A patch-based data augmentation strategy is also developed for the training of the proposed DCCMED model that increases the generalization ability of the network. Experiments are carried out on two publicly available datasets, namely Digital Retinal Images for Vessel Extraction (DRIVE) and Structured Analysis of the Retina (STARE). Evaluation criterions such as sensitivity (Se), specificity (Sp), accuracy (Acc), dice and area under the receiver operating characteristic curve (AUC) are used for verifying the effectiveness of the proposed method. The obtained results are compared with several supervised and unsupervised state-of-the-art methods based on AUC scores. The obtained results demonstrate that the proposed DCCMED model yields the best performance compared with the-state-of-the-art methods according to accuracy and AUC scores.

Cascaded deep convolutional encoder-decoder neural networks for efficient liver tumor segmentation.

Liver and hepatic tumor segmentation remains a challenging problem in Computer Tomography (CT) images analysis due to its shape variation and vague boundary. The general hypothesis says that deep learning methods produce improved results on medical image segmentation. This paper formulates the segmentation of liver tumor in CT abdominal images as a classification problem, and then solves it using a cascaded classifier framework based on deep convolutional neural networks. Two deep encoder-decoder convolutional neural networks (EDCNN) were constructed and trained to cascade segments of both the liver and lesions in CT images with limited image quantity. In other words, an EDCNN segments the liver image as the input for the training of a second EDCNN. The second EDCNN then segments the tumor regions within the liver ROI regions as predicted by the first EDCNN. Segmenting the hepatic tumor inside the liver ROI also significantly reduces false-positives. The proposed model was then tested using a public dataset (3DIRCADb), and several metrics were used in order to quantitatively evaluate its performance. The proposed method produced an average DICE score of 95.22% for the test set of CT images. The proposed method was then compared with some of the existing methods. The experimental results demonstrated that the proposed EDCNN achieved improved performance in segmentation accuracy over some existing methods.

Prediction of intrapartum fetal hypoxia considering feature selection algorithms and machine learning models.

INTRODUCTION: Cardiotocography (CTG) consists of two biophysical signals that are fetal heart rate (FHR) and uterine contraction (UC). In this research area, the computerized systems are usually utilized to provide more objective and repeatable results. MATERIALS AND METHODS: Feature selection algorithms are of great importance regarding the computerized systems to not only reduce the dimension of feature set but also to reveal the most relevant features without losing too much information. In this paper, three filters and two wrappers feature selection methods and machine learning models, which are artificial neural network (ANN), k-nearest neighbor (kNN), decision tree (DT), and support vector machine (SVM), are evaluated on a high dimensional feature set obtained from an open-access CTU-UHB intrapartum CTG database. The signals are divided into two classes as normal and hypoxic considering umbilical artery pH value (pH < 7.20) measured after delivery. A comprehensive diagnostic feature set forming the features obtained from morphological, linear, nonlinear, time-frequency and image-based time-frequency domains is generated first. Then, combinations of the feature selection algorithms and machine learning models are evaluated to achieve the most effective features as well as high classification performance. RESULTS: The experimental results show that it is possible to achieve better classification performance using lower dimensional feature set that comprises of more related features, instead of the high-dimensional feature set. The most informative feature subset was generated by considering the frequency of selection of the features by feature selection algorithms. As a result, the most efficient results were produced by selected only 12 relevant features instead of a full feature set consisting of 30 diagnostic indices and SVM model. Sensitivity and specificity were achieved as 77.40% and 93.86%, respectively. CONCLUSION: Consequently, the evaluation of multiple feature selection algorithms resulted in achieving the best results.

Essential Oil and Fatty Acid Composition of Endemic Gypsophila laricina Schreb. from Turkey.

OBJECTIVES: Gypsophila species have very high medicinal and commercial importance and contain interesting natural substances. However, there is no report on the essential oil or fatty acid composition of any Gypsophila species. This prompted us to investigate the essential oil and fatty acid composition of Gypsophila laricina Schreb. MATERIALS AND METHODS: Plant materials were collected during the flowering period. The essential oil composition of the aerial parts of G. laricina Schreb. was analyzed by gas chromatography and gas chromatography-mass spectrometry. The fatty acid compositions were analyzed by gas chromatography-mass spectrometry. RESULTS: Sixty-six and ten compounds were identified in the essential oil and fatty acid of G. laricina Schreb., respectively. The major components of the essential oil were hexadecanoic acid (27.03%) and hentriacontane (12.63%). The main compounds of the fatty acid were (Z,Z)-9,12- octadecadienoic acid methyl ester (18:2) 40.4%, (Z)-9-octadecenoic acid methyl ester (18:1) 35.0%, and hexadecanoic acid methyl ester (16:0) 13.0%. CONCLUSION: The results showed that the fatty acid composition is rich in polyunsaturated fatty acids. The essential oils of G. laricina Schreb. were dominated by fatty acid derivatives and n-alkanes. We think the results obtained from this research will stimulate further research on the chemistry of Gypsophila species.

A novel retinal vessel detection approach based on multiple deep convolution neural networks.

BACKGROUND AND OBJECTIVE: Computer aided detection (CAD) offers an efficient way to assist doctors to interpret fundus images. In a CAD system, retinal vessel (RV) detection is a crucial step to identify the retinal disease regions. However, RV detection is still a challenging problem due to variations in morphology of the vessels on noisy and low contrast fundus images. METHODS: In this paper, we formulate the detection task as a classification problem and solve it using a multiple classifier framework based on deep convolutional neural networks. The multiple deep convolutional neural network (MDCNN) is constructed and trained on fundus images with limited image quantity. The MDCNN is trained using an incremental learning strategy to improve the networks' performance. The final classification results are obtained from the voting procedure on the results of MDCNN. RESULTS: The MDCNN achieves better performance and significantly outperforms the state-of-the-art for automatic retinal vessel segmentation on the DRIVE dataset with 95.97% and 96.13% accuracy and 0.9726 and 0.9737 AUC (area below the operator receiver character curve) score on training and testing sets, respectively. Another public dataset, STARE, is also used to evaluate the proposed network. The experimental results demonstrate that the proposed MDCNN network achieves 95.39% accuracy and 0.9539 AUC score in STARE dataset. We further compare our result with several state-of-the-art methods based on AUC values. The comparison is shown that our proposal yields the third best AUC value. CONCLUSIONS: Our method yields the better performance in the compared the state of the art methods. In addition, our proposal has no preprocessing stage, and the input color fundus images are fed into the CNN directly.

Transfer learning based histopathologic image classification for breast cancer detection.

Breast cancer is one of the leading cancer type among women in worldwide. Many breast cancer patients die every year due to the late diagnosis and treatment. Thus, in recent years, early breast cancer detection systems based on patient's imagery are in demand. Deep learning attracts many researchers recently and many computer vision applications have come out in various environments. Convolutional neural network (CNN) which is known as deep learning architecture, has achieved impressive results in many applications. CNNs generally suffer from tuning a huge number of parameters which bring a great amount of complexity to the system. In addition, the initialization of the weights of the CNN is another handicap that needs to be handle carefully. In this paper, transfer learning and deep feature extraction methods are used which adapt a pre-trained CNN model to the problem at hand. AlexNet and Vgg16 models are considered in the presented work for feature extraction and AlexNet is used for further fine-tuning. The obtained features are then classified by support vector machines (SVM). Extensive experiments on a publicly available histopathologic breast cancer dataset are carried out and the accuracy scores are calculated for performance evaluation. The evaluation results show that the transfer learning produced better result than deep feature extraction and SVM classification.

Phrynahamzaoglui Koç & Budak (Caryophyllaceae), a new species from Central Anatolia, Turkey.

A new species Phrynahamzaoglui was discovered in Hekimhan (Turkey, Malatya province) where it grows on hillsides. The P.hamzaoglui and P.ortegioides were compared with each other in terms of their general morphology and seed micromorphology. Description, distribution, illustration and conservation status of the new species are given. Seed lateral and surface, cells, anticlinal cell walls, periclinal cell walls structures were examined by scanning electron microscopy. The images were captured with the 500×, 100×, and 40× objective lens for the details.

A novel microaneurysms detection approach based on convolutional neural networks with reinforcement sample learning algorithm.

Microaneurysms (MAs) are known as early signs of diabetic-retinopathy which are called red lesions in color fundus images. Detection of MAs in fundus images needs highly skilled physicians or eye angiography. Eye angiography is an invasive and expensive procedure. Therefore, an automatic detection system to identify the MAs locations in fundus images is in demand. In this paper, we proposed a system to detect the MAs in colored fundus images. The proposed method composed of three stages. In the first stage, a series of pre-processing steps are used to make the input images more convenient for MAs detection. To this end, green channel decomposition, Gaussian filtering, median filtering, back ground determination, and subtraction operations are applied to input colored fundus images. After pre-processing, a candidate MAs extraction procedure is applied to detect potential regions. A five-stepped procedure is adopted to get the potential MA locations. Finally, deep convolutional neural network (DCNN) with reinforcement sample learning strategy is used to train the proposed system. The DCNN is trained with color image patches which are collected from ground-truth MA locations and non-MA locations. We conducted extensive experiments on ROC dataset to evaluate of our proposal. The results are encouraging.

In vitro genotoxic effects of four Helichrysum species in human lymphocytes cultures.

Helichrysum sanguineum, Helichrysum pamphylicum, Helichrysum orientale, Helichrysum noeanum (Asteraceae) are medicinal plants. For centuries, they have been used as tea in Turkey because of their medicinal properties. So far no scientific evidence has been found in a literature survey regarding the genotoxic effects of these plants. This work evaluated the genotoxic effects on human lymphocyte cultures induced by methanol extracts of these plants, assayed in different concentrations (0.01, 0.05, 0.1, 0.5 and 1 mg/mL). According to the results, Helichrysum noeanum, Helichrysum pamphylicum and Helichrysum sanguineum induced the formation of micronuclei and decreased the mitotic and replication indexes. Helichrysum orientale did not affect these parameters, whereas Helichrysum noeanum, Helichrysum pamphylicum and Helichrysum sanguineum were clearly genotoxic. They should therefore not be used freely in alternative medicine, although their antiproliferative activity may suggest antimitotic and anticarcinogenic properties. Helichrysum orientale could be used in alternative medicine.

In Vitro Genotoxic Effects of Four Helichrysum Species in Human Lymphocytes Cultures

Helichrysum sanguineum, Helichrysum pamphylicum, Helichrysum orientale, Helichrysum noeanum (Asteraceae) are medicinal plants. For centuries, they have been used as tea in Turkey because of their medicinal properties. So far no scientifc evidence has been found in a literature survey regarding the genotoxic effects of these plants. This work evaluated the genotoxic effects on human lymphocyte cultures induced by methanol extracts of these plants, assayed in different concentrations (0.01, 0.05, 0.1, 0.5 and 1 mg/mL). According to the results, Helichrysum noeanum, Helichrysum pamphylicum and Helichrysum sanguineum induced the formation of micronuclei and decreased the mitotic and replication indexes. Helichrysum orientale did not affect these parameters, whereas Helichrysum noeanum, Helichrysum pamphylicum and Helichrysum sanguineum were clearly genotoxic. They should therefore not be used freely in alternative medicine, although their antiproliferative activity may suggest antimitotic and anticarcinogenic properties. Helichrysum orientale could be used in alternative medicine.

Cytogenetic effects of nine Helichrysum taxa in human lymphocytes culture.

Helichrysum Mill. (Asteraceae) species have been used in folk medicine for thousands of years in the world. The in vitro cytogenetic effects in human lymphocytes of nine Helichrysum taxa used in Turkey folk medicine were investigated. Blood samples were obtained from healthy donors, non-smoking volunteers, which were incubated and exposed to increasing concentrations of methanol extracts of Helichrysum taxa (0.01, 0.05, 0.1, 0.5 and 1 mg/mL). The inhibitory effects of H. stoechas (L.) Moench subsp. barrelieri (Ten.) Nyman, H. armenium DC. subsp. armenium, H. armenium DC. subsp. araxinum (Kirp.) Takht., H. plicatum DC. subsp. plicatum, H. compactum Boiss. and H. artvinense P.H.Davis & Kupicha on the mitotic index and replication index indicate that these taxa can have genotoxic and mutagenic effects. They should therefore not be used freely in alternative medicine although their antiproliferative activity may suggest anticarcinogenic properties. Increase effects of H. stoechas subsp. barrelieri, H. armenium subsp. armenium, H. armenium subsp. araxinum, H. chasmolycicum P.H.Davis, H. plicatum subsp. plicatum, H. compactum and H. artvinense on the micronucleus rates showed that these taxa can have genotoxic and carcinogenic effects.