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Abstract Endocrine cells in the fundic stomach of Clarias gariepinus were characterized in this work using transmission electron microscopy, immunohistochemistry, and histochemistry. Performic acid mixed with alcian blue pH2.5 and silver stain were among the histochemical stains used for endocrine cells. Endocrine cells can be found in the epithelium, lamina propria, submucosa, muscular layer, serosa, and the area between the stomach glands. Endocrine cells with one or more nuclei were found. Endocrine cells were studied using CD3, CD21, and CD68 in an immunohistochemistry analysis. The expression of the lymphocyte marker CD3 by endocrine cells is remarkable. In addition, they had a strong immunological response to CD21 and CD68, which are characteristics of phagocytic cells. Granules of varied sizes and electron densities are packed densely into the cytoplasm of the cells, as seen by transmission electron microscopy. We propose that endocrine cells play a crucial role in immune defense. The role of endocrine cells in the gut’s immune system is an area that needs further investigation.

The accurate detection of brain tumors through medical imaging is paramount for precise diagnoses and effective treatment strategies. In this study, we introduce an innovative and robust methodology that capitalizes on the transformative potential of the Swin Transformer architecture for meticulous brain tumor image classification. Our approach handles the classification of brain tumors across four distinct categories: glioma, meningioma, non-tumor, and pituitary, leveraging a dataset comprising 2,870 images. Employing the Swin Transformer architecture, our method intricately integrates a multifaceted pipeline encompassing sophisticated preprocessing, intricate feature extraction mechanisms, and a highly nuanced classification framework. Utilizing 21 matrices for performance evaluation across all four classes, these matrices provide a detailed insight into the model’s behavior throughout the learning process, furthermore showcasing a graphical representation of confusion matrix, training and validation loss and accuracy. The standout performance parameter, accuracy, stands at an impressive 97%. This achievement outperforms established models like CNN, DCNN, ViT, and their variants in brain tumor classification. Our methodology’s robustness and exceptional accuracy showcase its potential as a pioneering model in this domain, promising substantial advancements in accurate tumor identification and classification, thereby contributing significantly to the landscape of medical image analysis.

] Accurate classification of brain tumors is essential for effective medical diagnosis and treatment planning. Traditional approaches rely on convolutional neural networks (CNNs) for tumor detection, but they often suffer from high computational demands due to the large number of parameters. In this paper, we propose a novel approach for brain tumor classification using involutional neural networks (InvNets), which are designed to mitigate the parameter-intensive nature of CNNs. Unlike the spatial-agnostic and channel-specific convolution kernel, the involution kernel is location-specific and channel-agnostic. This location-specific operation allows the network to adapt to various visual patterns with respect to different spatial locations, enhancing its ability to capture intricate features within the medical images. Our study focuses on a four-class brain tumor classification problem, aiming to differentiate between different tumor types based on medical imaging data. In a comparative analysis, we demonstrate that conventional CNNs require over 4 million parameters, whereas our proposed InvNets require less than 0.2 million parameters, making them more efficient and resource-friendly. The evaluation of both CNNs and InvNets is carried out using standard performance matrices: accuracy, precision, recall, F1 score, and AUC-ROC values. Our findings reveal that the InvNets consistently outperform traditional CNNs. The InvNet architecture achieves an impressive 92% accuracy rate, showcasing its potential for accurate brain tumor classification. This improved accuracy, combined with the reduced parameter count, highlights the effectiveness of InvNets for medical image analysis tasks, especially in scenarios with limited computational resources.

Brain tumor has become one of the fatal causes of death worldwide in recent years, affecting many individuals annually and resulting in loss of lives. Brain tumors are characterized by the abnormal or irregular growth of brain tissues that can spread to nearby tissues and eventually throughout the brain. Although several traditional machine learning and deep learning techniques have been developed for detecting and classifying brain tumors, they do not always provide an accurate and timely diagnosis. This study proposes a conditional generative adversarial network (CGAN) that leverages the fine-tuning of a convolutional neural network (CNN) to achieve more precise detection of brain tumors. The CGAN comprises two parts, a generator and a discriminator, whose outputs are used as inputs for fine-tuning the CNN model. The publicly available dataset of brain tumor MRI images on Kaggle was used to conduct experiments for Datasets 1 and 2. Statistical values such as precision, specificity, sensitivity, F1-score, and accuracy were used to evaluate the results. Compared to existing techniques, our proposed CGAN model achieved an accuracy value of 0.93 for Dataset 1 and 0.97 for Dataset 2.

Background: SARS-CoV-2, an emerged strain of corona virus family became almost serious health concern worldwide. Despite vaccines availability, reports suggest the occurrence of SARS-CoV-2 infection even in a vaccinated population. With frequent evolution and expected multiple COVID-19 waves, improved preventive, diagnostic, and treatment measures are required. In recent times, phytochemicals have gained attention due to their therapeutic characteristics and are suggested as alternative and complementary treatments for infectious diseases. This present study aimed to identify potential inhibitors against reported protein targets of SARS-CoV-2. Methodology: We computationally investigated potential SARS-CoV-2 protein targets from the literature and collected druggable phytochemicals from Indian Medicinal Plants, Phytochemistry and Therapeutics (IMPPAT) database. Further, we implemented a systematic workflow of molecular docking, dynamic simulations and generalized born surface area free-energy calculations (MM-GBSA). Results: Extensive literature search and assessment of 1508 articles identifies 13 potential SARS-CoV-2 protein targets. We screened 501 druggable phytochemicals with proven biological activities. Analysis of 6513(501 *13) docked phytochemicals complex, 26 were efficient against SARS-CoV-2. Amongst, 4,8-dihydroxysesamin and arboreal from Gmelina arborea were ranked potential against most of the targets with binding energy ranging between − 10.7 to − 8.2 kcal/mol. Additionally, comparative docking with known drugs such as arbidol (−6.6 to −5.1 kcal/mol), favipiravir (−5.5 to −4.5 kcal/mol), hydroxychloroquine (−6.5 to −5.1 kcal/mol), and remedesivir (−8.0 to −5.3 kcal/mol) revealed equal/less affinity than 4,8-dihydroxysesamin and arboreal. Interestingly, the nucleocapsid target was found commonly inhibited by 4,8-dihydroxysesamin and arboreal. Molecular dynamic simulation and Molecular mechanics generalized born surface area (MM-GBSA)calculations reflect that both the compounds possess high inhibiting potential against SARS-CoV-2 including the recently emerged Omicron variant (B.1.1.529). Conclusion: Overall our study imparts the usage of phytochemicals as antiviral agents for SARS-CoV-2 infection. Additional in vitro and in vivo testing of these phytochemicals is required to confirm their potency.

This paper presents a comprehensive study on the classification of brain tumor images using five pre-trained vision transformer (ViT) models, namely R50-ViT-l16, ViT-l16, ViT-l32, ViT-b16, and ViT-b32, employing a fine-tuning approach. The objective of this study is to advance the state-of-the-art in brain tumor classification by harnessing the power of these advanced models. The dataset utilized for experimentation consists of a total of 4855 images in the training set and 857 images in the testing set, encompassing four distinct tumor classes. The performance evaluation of each model is conducted through an extensive analysis encompassing precision, recall, F1-score, accuracy, and confusion matrix metrics. Among the models assessed, ViT-b32 demonstrates exceptional performance, achieving a high accuracy of 98.24% in accurately classifying brain tumor images. Notably, the obtained results outperform existing methodologies, showcasing the efficacy of the proposed approach. The contributions of this research extend beyond conventional methods, as it not only employs cutting-edge ViT models but also surpasses the performance of existing approaches for brain tumor image classification. This study not only demonstrates the potential of ViT models in medical image analysis but also provides a benchmark for future research in the field of brain tumor classification.

Avian pathogenic Escherichia coli is one of the principal causes of heavy economic losses to the poultry industry. Little is known about the underlying mechanisms, particularly the potential role of immunoglobulin A and the DNA damage, involving the beneficial effects of dietary supplementation of probiotics and prebiotics in avian colibacillosis. The current study investigated the potential effects of probiotic and prebiotic dietary supplementation on E. coli-infected broiler chicks. A total of 120 1-day-old unsexed Hubbard chicks were divided into six groups: Group 1 was considered as a negative control; Group 2 was supplemented with 1 g/kg feed of Lactobacillus plantarum; Group 3 was supplemented with amylase enzyme; Group 4 served as a positive control infected orally by E. coli O78; Group 5 was supplemented with L. plantarum from 1-day-old chicken and then infected orally with E. coli O78; and Group 6 was supplemented with amylase enzyme from 1-day old chicken and then infected orally with E. coli O78. For all examined groups, the experimental period lasted for 42 days. The E. coli-infected group revealed a decrease in body performance parameters with a significant increase in the liver enzymes and renal function tests. The same group recorded a significant decrease in serum total proteins, albumins, and globulins, and the alteration of immunological parameters, antioxidant enzymes, oxidative stress parameters, and comet assay revealed highly damaged DNA in the liver and the intestine. By histopathological examination, a series of histopathological changes in the liver, the kidney, and the intestine were observed. The infected chick pretreated with probiotics or prebiotics demonstrated an improvement in body performance parameters besides a significant decrease in the hepatic enzymes and renal function tests. We noticed that, in treated groups, there was a significant increase in serum total proteins in the serum albumin and globulin levels, immunological parameters, and antioxidant enzymes. Furthermore, DNA damage and histopathological changes within hepatic, renal, and intestinal tissues were markedly diminished in the treated groups compared with the infected group. We concluded that the adverse effects of E. coli could be modulated through the chemopreventive administration of probiotics and prebiotics.

A brain tumor is a significant health concern that directly or indirectly affects thousands of people worldwide. The early and accurate detection of brain tumors is vital to the successful treatment of brain tumors and the improved quality of life of the patient. There are several imaging techniques used for brain tumor detection. Among these techniques, the most common are MRI and CT scans. To overcome the limitations associated with these traditional techniques, computer-aided analysis of brain images has gained attention in recent years as a promising approach for accurate and reliable brain tumor detection. In this study, we proposed a fine-tuned vision transformer model that uses advanced image processing and deep learning techniques to accurately identify the presence of brain tumors in the input data images. The proposed model FT-ViT involves several stages, including the processing of data, patch processing, concatenation, feature selection and learning, and fine tuning. Upon training the model on the CE-MRI dataset containing 5712 brain tumor images, the model could accurately identify the tumors. The FT-Vit model achieved an accuracy of 98.13%. The proposed method offers high accuracy and can significantly reduce the workload of radiologists, making it a practical approach in medical science. However, further research can be conducted to diagnose more complex and rare types of tumors with more accuracy and reliability.

Nowadays, brain tumors have become a leading cause of mortality worldwide. The brain cells in the tumor grow abnormally and badly affect the surrounding brain cells. These cells could be either cancerous or non-cancerous types, and their symptoms can vary depending on their location, size, and type. Due to its complex and varying structure, detecting and classifying the brain tumor accurately at the initial stages to avoid maximum death loss is challenging. This research proposes an improved fine-tuned model based on CNN with ResNet50 and U-Net to solve this problem. This model works on the publicly available dataset known as TCGA-LGG and TCIA. The dataset consists of 120 patients. The proposed CNN and fine-tuned ResNet50 model are used to detect and classify the tumor or no-tumor images. Furthermore, the U-Net model is integrated for the segmentation of the tumor regions correctly. The model performance evaluation metrics are accuracy, intersection over union, dice similarity coefficient, and similarity index. The results from fine-tuned ResNet50 model are IoU: 0.91, DSC: 0.95, SI: 0.95. In contrast, U-Net with ResNet50 outperforms all other models and correctly classified and segmented the tumor region.

The global distribution of breast cancer and the opportunistic nature of the parasite have resulted in many patients with breast cancer becoming infected with toxoplasmosis. However, very limited information is available about the potential effects of tamoxifen on chronic toxoplasmosis and its contribution to the reactivation of the latent infection. The present study investigated the potential effects of tamoxifen on chronic toxoplasmosis in animal models (Swiss albino mice). Following induction of chronic toxoplasmosis and treatment with the drug for 14 and 28 days, the anti-parasitic effects of tamoxifen were evaluated by parasitological assessment and counting of Toxoplasma cysts. In addition, the effects of the drug on the parasite load were evaluated and quantitated using TaqMan real-time quantitative PCR followed by investigation of the major histopathological changes and immunohistochemical findings. Interestingly, tamoxifen increased the parasite burden on animals treated with the drug during 14 and 28 days as compared with the control group. The quantification of the DNA concentrations of Toxoplasma P29 gene after the treatment with the drug revealed a higher parasite load in both treated groups vs. control groups. Furthermore, treatment with tamoxifen induced a series of histopathological and immunohistochemical changes in the kidney, liver, brain, and uterus, revealing the exacerbating effect of tamoxifen against chronic toxoplasmosis. These changes were represented by the presence of multiple T. gondii tissue cysts in the lumen of proximal convoluted tubules associated with complete necrosis in their lining epithelium of the kidney section. Meanwhile, liver tissue revealed multiple T. gondii tissue cysts in hepatic parenchyma which altered the structure of hepatocytes. Moreover, clusters of intracellular tachyzoites were observed in the lining epithelium of endometrium associated with severe endometrial necrosis and appeared as diffuse nuclear pyknosis combined with sever mononuclear cellular infiltration. Brain tissues experienced the presence of hemorrhages in pia mater and multiple T. gondii tissue cysts in brain tissue. The severity of the lesions was maximized by increasing the duration of treatment. Collectively, the study concluded novel findings in relation to the potential role of tamoxifen during chronic toxoplasmosis. These findings are very important for combating the disease, particularly in immunocompromised patients which could be life-threatening.

Trypanosomiasis and leishmaniasis are a group of neglected parasitic diseases caused by several species of parasites belonging to the family Trypansomatida. The present study investigated the antitrypanosomal and antileishmanial activity of chalcones and flavanones from Polygonum salicifolium, which grows in the wetlands of Iraq. The phytochemical evaluation of the plant yielded two chalcones, 2′,4′-dimethoxy-6′-hydroxychalcone and 2′,5′-dimethoxy-4′,6′-dihydroxychalcone, and two flavanones, 5,7-dimethoxyflavanone and 5,8-dimethoxy-7-hydroxyflavanone. The chalcones showed a good antitrypanosomal and antileishmanial activity while the flavanones were inactive. The EC50 values for 2′,4′-dimethoxy-6′-hydroxychalcone against Trypanosoma brucei brucei (0.5 μg/mL), T. congolense (2.5 μg/mL), and Leishmania mexicana (5.2 μg/mL) indicated it was the most active of the compounds. None of the compounds displayed any toxicity against a human cell line, even at 100 µg/mL, or cross-resistance with first line clinical trypanocides, such as diamidines and melaminophenyl arsenicals. Taken together, our study provides significant data in relation to the activity of chalcones and flavanones from P. salicifolium against both parasites in vitro. Further future research is suggested in order to investigate the mode of action of the extracted chalcones against the parasites.

Only a single drug against schistosomiasis is currently available and new drug development is urgently required but very few drug targets have been validated and characterised. However, regulatory systems including cyclic nucleotide metabolism are emerging as primary candidates for drug discovery. Here, we report the cloning of ten cyclic nucleotide phosphodiesterase (PDE) genes of S. mansoni, out of a total of 11 identified in its genome. We classify these PDEs by homology to human PDEs. Male worms displayed higher expression levels for all PDEs, in mature and juvenile worms, and schistosomula. Several functional complementation approaches were used to characterise these genes. We constructed a Trypanosoma brucei cell line in which expression of a cAMP-degrading PDE complements the deletion of TbrPDEB1/B2. Inhibitor screens of these cells expressing only either SmPDE4A, TbrPDEB1 or TbrPDEB2, identified highly potent inhibitors of the S. mansoni enzyme that elevated the cellular cAMP concentration. We further expressed most of the cloned SmPDEs in two pde1Δ/pde2Δ strains of Saccharomyces cerevisiae and some also in a specialised strain of Schizosacharomyces pombe. Five PDEs, SmPDE1, SmPDE4A, SmPDE8, SmPDE9A and SmPDE11 successfully complemented the S. cerevisiae strains, and SmPDE7var also complemented to a lesser degree, in liquid culture. SmPDE4A, SmPDE8 and SmPDE11 were further assessed in S. pombe for hydrolysis of cAMP and cGMP; SmPDE11 displayed considerable preferrence for cGMP over cAMP. These results and tools enable the pursuit of a rigorous drug discovery program based on inhibitors of S. mansoni PDEs.

Mutations in the Trypanosoma brucei aquaporin AQP2 are associated with resistance to pentamidine and melarsoprol. We show that TbAQP2 but not TbAQP3 was positively selected for increased pore size from a common ancestor aquaporin. We demonstrate that TbAQP2’s unique architecture permits pentamidine permeation through its central pore and show how specific mutations in highly conserved motifs affect drug permeation. Introduction of key TbAQP2 amino acids into TbAQP3 renders the latter permeable to pentamidine. Molecular dynamics demonstrates that permeation by dicationic pentamidine is energetically favourable in TbAQP2, driven by the membrane potential, although aquaporins are normally strictly impermeable for ionic species. We also identify the structural determinants that make pentamidine a permeant although most other diamidine drugs are excluded. Our results have wide-ranging implications for optimising antitrypanosomal drugs and averting cross-resistance. Moreover, these new insights in aquaporin permeation may allow the pharmacological exploitation of other members of this ubiquitous gene family.