Multi-model deep learning approach for segmentation of teeth and periapical lesions on pantomographs.

INTRODUCTION: The fields of medicine and dentistry are beginning to integrate artificial intelligence (AI) in diagnostics. This may reduce subjectivity and improve the accuracy of diagnoses and treatment planning. Current evidence on pathosis detection on pantomographs (PGs) indicates the presence or absence of disease in the entire radiographic image, with little evidence of the relation of periapical pathosis to the causative tooth. OBJECTIVE: To develop a deep learning (DL) AI model for the segmentation of periapical pathosis and its relation to teeth on PGs. METHOD: 250 PGs were manually annotated by subject experts to lay down the ground truth for training AI algorithms on the segmentation of periapical pathosis. Two approaches were used for lesion detection: Multi-models 1 and 2, using U-net and Mask RCNN algorithms, respectively. The resulting segmented lesions generated on the testing data set were superimposed with results of teeth segmentation and numbering algorithms trained separately to relate lesions to causative teeth. Hence, both multi-model approaches related periapical pathosis to the causative teeth on PGs. RESULTS: The performance metrics of lesion segmentation carried out by U-net are as follows: Accuracy = 98.1%, precision = 84.5%, re-call = 80.3%, F-1 score = 82.2%, dice index = 75.2%, and Intersection over Union = 67.6%. Mask RCNN carried out lesion segmentation with an accuracy of 46.7%, precision of 80.6%, recall of 55%, and F-1 score of 63.1%. CONCLUSION: In this study, the multi-model approach successfully related periapical pathosis to the causative tooth on PGs. However, U-net outperformed Mask RCNN in the tasks performed, suggesting that U-net will remain the standard for medical image segmentation tasks. Further training of the models on other findings and an increased number of images will lead to the automation of the detection of common radiographic findings in the dental diagnostic workflow.

Impact of hypoxia on glucose metabolism and hypoxia signaling pathways in juvenile horseshoe crabs Tachypleus tridentatus.

Marine hypoxia poses a significant challenge in the contemporary marine environment. The horseshoe crab, an ancient benthic marine organism, is confronted with the potential threat of species extinction due to hypoxia, making it an ideal candidate for studying hypoxia tolerance mechanisms. In this experiment, juvenile Tachypleus tridentatus were subjected to a 21-day trial at DO:2 mg/L (hypoxia) and DO:6 mg/L conditions. The experimental timeline included a 14-day exposure phase followed by a 7-day recovery period. Sampling occurred on days 0, 7, 14, and 21, where the period from day 14 to day 21 corresponds to seven days of recuperation. Several enzymatic activities of important proteins throughout this investigation were evaluated, such as succinate dehydrogenase (SDH), phosphofructokinase (PFK), hexokinase (HK), lactate dehydrogenase (LDH), and pyruvate kinase (PK). Concurrently, the relative expression of hexokinase-1 (HK), hypoxia-inducible factor 1-alpha inhibitor (FIH), and hypoxia-inducible factor 1-alpha (HIF-1α), pyruvate dehydrogenase phosphatase (PDH), succinate dehydrogenase assembly factor 4 (SDH), and Glucose-6-phosphatase (G6Pase) were also investigated. These analyses aimed to elucidate alterations in the hypoxia signaling pathway and respiratory energy metabolism. It is revealed that juvenile T. tridentatus initiated the HIF pathway under hypoxic conditions, resulting in an upregulation of HIF-1α and FIH-1 gene expression, which in turn, influenced a shift in metabolic patterns. Particularly, the activity of glycolysis-related enzymes was promoted significantly, including PK, HK, PKF, LDH, and the related HK gene. In contrast, enzymes linked to aerobic respiration, PDH, and SDH, as well as the related PDH and SDH genes, displayed down-regulation, signifying a transition from aerobic to anaerobic metabolism. Additionally, the activity of gluconeogenesis-related enzymes such as PK and G6Pase gene expression were significantly elevated, indicating the activation of gluconeogenesis and glycogenolysis pathways. Consequently, juvenile T. tridentatus demonstrated an adaptive response to hypoxic conditions, marked by changes in respiratory energy metabolism modes and the activation of hypoxia signaling pathways.

Implementation of transfer learning for the segmentation of human mesenchymal stem cells-A validation study.

INTRODUCTION: Stem cell therapy has been gaining interest in the regeneration rather than repair of lost human tissues. However, the manual analysis of stem cells prior to implantation is a cumbersome task that can be automated to improve the efficiency and accuracy of this process. OBJECTIVE: To develop a Deep Learning (DL) algorithm for segmentation of human mesenchymal stem cells (MSCs) on micrographic images and to validate its performance relative to the ground truth laid down via annotation. METHODOLOGY: Pre-trained DeepLab algorithms were trained on annotated images of human MSCs obtained from the open-source EVICAN dataset. This dataset comprises of partially annotated images; a limitation that is overcome by blurring backgrounds of these images which consequently blurs the unannotated cells. Two algorithms were trained on the two different kinds of images from this dataset; with blurred and normal backgrounds, respectively. Algorithm 1 was trained on 139 images with blurred backgrounds and algorithm 2 was trained on 37 images from the same dataset with normal backgrounds to replicate real-life scenarios. RESULTS: The performance metrics of algorithm 1 included accuracy of 99.22%, dice co-efficient of 99.66% and Intersection over Union (IoU) score of 0.84. Algorithm 2 was 96.34% accurate with dice co-efficient and IoU scores of 98.39% and 0.48, respectively. CONCLUSION: Both algorithms showed adequate performance in the segmentation of human MSCs with performance metrics close to the ground truth. However, algorithm 2 has better clinical applicability, even with smaller dataset and relatively lower performance metrics.

Vaccine Adjuvants: Selection Criteria, Mechanism of Action Associated with Immune Responses and Future Directions.

The most effective method to minimize the prevalence of infectious diseases is vaccination. Vaccines enhance immunity and provide protection against different kinds of infections. Subunit vaccines are safe and less toxic, but due to their lower immunogenicity, they need adjuvants to boost the immune system. Adjuvants are small particles/molecules integrated into a vaccine to enhance the immunogenic feedback of antigens. They play a significant role to enhance the potency and efficiency of vaccines. There are several types of adjuvants with different mechanisms of action; therefore, improved knowledge of their immunogenicity will help develop a new generation of adjuvants. Many trials have been designed using different kinds of vaccine adjuvants to examine their safety and efficacy, but in practice, only a few have entered in animal and human clinical trials. However, for the development of safe and effective vaccines, it is important to have adequate knowledge of the side effects and toxicity of different adjuvants. The current review discussed the adjuvants which are available for producing modern vaccines as well as some new classes of adjuvants in clinical trials.

Fish-Pak: Fish species dataset from Pakistan for visual features based classification.

Fishes are most diverse group of vertebrates with more than 33000 species. These are identified based on several visual characters including their shape, color and head. It is difficult for the common people to directly identify the fish species found in the market. Classifying fish species from images based on visual characteristics using computer vision and machine learning techniques is an interesting problem for the researchers. However, the classifier's performance depends upon quality of image dataset on which it has been trained. An imagery dataset is needed to examine the classification and recognition algorithms. This article exhibits Fish-Pak: an image dataset of 6 different fish species, captured by a single camera from different pools located nearby the Head Qadirabad, Chenab River in Punjab, Pakistan. The dataset Fish-Pak are quite useful to compare various factors of classifiers such as learning rate, momentum and their impact on the overall performance. Convolutional Neural Network (CNN) is one of the most widely used architectures for image classification based on visual features. Six data classes i.e. Ctenopharyngodon idella (Grass carp), Cyprinus carpio (Common carp), Cirrhinus mrigala (Mori), Labeo rohita (Rohu), Hypophthalmichthys molitrix (Silver carp), and Catla (Thala), with a different number of images, have been included in the dataset. Fish species are captured by one camera to ensure the fair environment to all data. Fish-Pak is hosted by the Zoology Lab under the mutual affiliation of the Department of Computer Science and the Department of Zoology, University of Gujrat, Gujrat, Pakistan.