Identifying key factors for predicting O6-Methylguanine-DNA methyltransferase status in adult patients with diffuse glioma: a multimodal analysis of demographics, radiomics, and MRI by variable Vision Transformer.

PURPOSE: This study aimed to perform multimodal analysis by vision transformer (vViT) in predicting O6-methylguanine-DNA methyl transferase (MGMT) promoter status among adult patients with diffuse glioma using demographics (sex and age), radiomic features, and MRI. METHODS: The training and test datasets contained 122 patients with 1,570 images and 30 patients with 484 images, respectively. The radiomic features were extracted from enhancing tumors (ET), necrotic tumor cores (NCR), and the peritumoral edematous/infiltrated tissues (ED) using contrast-enhanced T1-weighted images (CE-T1WI) and T2-weighted images (T2WI). The vViT had 9 sectors; 1 demographic sector, 6 radiomic sectors (CE-T1WI ET, CE-T1WI NCR, CE-T1WI ED, T2WI ET, T2WI NCR, and T2WI ED), 2 image sectors (CE-T1WI, and T2WI). Accuracy and area under the curve of receiver-operating characteristics (AUC-ROC) were calculated for the test dataset. The performance of vViT was compared with AlexNet, GoogleNet, VGG16, and ResNet by McNemar and Delong test. Permutation importance (PI) analysis with the Mann-Whitney U test was performed. RESULTS: The accuracy was 0.833 (95% confidence interval [95%CI]: 0.714-0.877) and the area under the curve of receiver-operating characteristics was 0.840 (0.650-0.995) in the patient-based analysis. The vViT had higher accuracy than VGG16 and ResNet, and had higher AUC-ROC than GoogleNet (p<0.05). The ED radiomic features extracted from the T2-weighted image demonstrated the highest importance (PI=0.239, 95%CI: 0.237-0.240) among all other sectors (p<0.0001). CONCLUSION: The vViT is a competent deep learning model in predicting MGMT status. The ED radiomic features of the T2-weighted image demonstrated the most dominant contribution.

A novel Skin lesion prediction and classification technique: ViT-GradCAM.

BACKGROUND: Skin cancer is one of the highly occurring diseases in human life. Early detection and treatment are the prime and necessary points to reduce the malignancy of infections. Deep learning techniques are supplementary tools to assist clinical experts in detecting and localizing skin lesions. Vision transformers (ViT) based on image segmentation classification using multiple classes provide fairly accurate detection and are gaining more popularity due to legitimate multiclass prediction capabilities. MATERIALS AND METHODS: In this research, we propose a new ViT Gradient-Weighted Class Activation Mapping (GradCAM) based architecture named ViT-GradCAM for detecting and classifying skin lesions by spreading ratio on the lesion's surface area. The proposed system is trained and validated using a HAM 10000 dataset by studying seven skin lesions. The database comprises 10 015 dermatoscopic images of varied sizes. The data preprocessing and data augmentation techniques are applied to overcome the class imbalance issues and improve the model's performance. RESULT: The proposed algorithm is based on ViT models that classify the dermatoscopic images into seven classes with an accuracy of 97.28%, precision of 98.51, recall of 95.2%, and an F1 score of 94.6, respectively. The proposed ViT-GradCAM obtains better and more accurate detection and classification than other state-of-the-art deep learning-based skin lesion detection models. The architecture of ViT-GradCAM is extensively visualized to highlight the actual pixels in essential regions associated with skin-specific pathologies. CONCLUSION: This research proposes an alternate solution to overcome the challenges of detecting and classifying skin lesions using ViTs and GradCAM, which play a significant role in detecting and classifying skin lesions accurately rather than relying solely on deep learning models.

Emission-based sensing of cobalt (II) and vitamin B12 via a bis-indole derivative.

In this study, a bis-indole compound was synthesized, characterized by 1H NMR, Fourier transform infrared, and mass spectroscopic measurements and used as a selective and efficient probe for the spectrofluorimetric analysis of Co (II). The cobalt-induced quenching in the emission maximum at 567 nm was considered as the analytical signal in calibration studies. When encapsulated in a polymethyl methacrylate (PMMA) matrix, the bis-indole compound exhibited a limit of detection (LOD) of 3.60 × 10-11 M for Co (II). Vitamin B12, which contains a cobalt ion in the center of a corrin ring in its structure, was also successfully quantified using the same probe. The bis-indole compound showed a linear response based on quenching for increasing concentrations of vitamin B12, partially mimicking the contracted tetrapyrrole ring found naturally in the center of vitamin B12. The LOD for vitamin B12 was found to be 76 nm. Promising photophysical properties of the proposed probe, including high molar extinction coefficient, considerable quantum yield (0.46 and 0.64 in tetrahydrofuran and PMMA, respectively), high Stoke's shift and satisfactory photostability, make it a good choice for fluorescence-based Co (II) determination. The ML3-type stoichiometry of the complex between the dye and cobalt was elucidated both by Job's method and by high-resolution mass spectrometry (HR-MS).

Automated classification of choroidal neovascularization, diabetic macular edema, and drusen from retinal OCT images using vision transformers: a comparative study.

Classifying retinal diseases is a complex problem because the early problematic areas of retinal disorders are quite small and conservative. In recent years, Transformer architectures have been successfully applied to solve various retinal related health problems. Age-related macular degeneration (AMD) and diabetic macular edema (DME), two prevalent retinal diseases, can cause partial or total blindness. Diseases therefore require an early and accurate detection. In this study, we proposed Vision Transformer (ViT), Tokens-To-Token Vision Transformer (T2T-ViT) and Mobile Vision Transformer (Mobile-ViT) algorithms to detect choroidal neovascularization (CNV), drusen, and diabetic macular edema (DME), and normal using optical coherence tomography (OCT) images. The predictive accuracies of ViT, T2T-ViT and Mobile-ViT achieved on the dataset for the classification of OCT images are 95.14%, 96.07% and 99.17% respectively. Experimental results obtained from ViT approaches showed that Mobile-ViT have superior performance with regard to classification accuracy in comparison with the others. Overall, it has been observed that ViT architectures have the capacity to classify with high accuracy in the diagnosis of retinal diseases.

A Dynamic Position Embedding-Based Model for Student Classroom Complete Meta-Action Recognition.

The precise recognition of entire classroom meta-actions is a crucial challenge for the tailored adaptive interpretation of student behavior, given the intricacy of these actions. This paper proposes a Dynamic Position Embedding-based Model for Student Classroom Complete Meta-Action Recognition (DPE-SAR) based on the Video Swin Transformer. The model utilizes a dynamic positional embedding technique to perform conditional positional encoding. Additionally, it incorporates a deep convolutional network to improve the parsing ability of the spatial structure of meta-actions. The full attention mechanism of ViT3D is used to extract the potential spatial features of actions and capture the global spatial-temporal information of meta-actions. The proposed model exhibits exceptional performance compared to baseline models in action recognition as observed in evaluations on public datasets and smart classroom meta-action recognition datasets. The experimental results confirm the superiority of the model in meta-action recognition.

Vitamin D ameliorates diethylnitrosamine-induced liver preneoplasia: A pivotal role of CYP3A4/CYP2E1 via DPP-4 enzyme inhibition.

Growing evidence has indicated that vitamin D (Vit D) regulates cell proliferation and differentiation in cancer cells. Accordingly, the present study was conducted to investigate the possible beneficial effects of Vit D on diethylnitrosamine (DEN)-induced liver preneoplasia. The effect of Vit D on HepG2 cells was investigated using MTT assay. Additionally, liver preneoplasia was induced in Swiss male albino mice by giving overnight fasted animals 5 consecutive doses of DEN (75 mg/kg/week). Oral treatment with Vit D (200 IU/kg/day) was initiated either 2 weeks before DEN (first protocol) or 1 week after the first dose of DEN injection (second protocol). At the end of the experiment, tissue levels of GGT, DPP-4, TNF-α, IL-6, CYP2E1, and CYP3A4 were also estimated. Moreover, the histopathological study of liver tissue and immunohistochemical detection of GST-P, PCNA, and NF-κB were performed. Vit D exerted a significant cytotoxic effect on HepG2 cells via significantly increasing BAX, p53, and BAX/Bcl2 ratio, and significantly decreasing Bcl2 mRNA expression. In both in vivo protocols, Vit D was capable of normalizing relative liver weight, PCNA, altered hepatocellular foci, and ductular proliferation. Moreover, Vit D significantly reduced the DEN-induced elevation of AST, ALT, ALP, GGT, DDP-4, TNF-α, IL-6, CYP2E1, liver DNA damage, GST-P, NF-κB, nuclear hyperchromasia/pleomorphism, cholestasis, and inflammatory cell aggregates, but significantly increased CYP3A4 content. In conculsion, current results reflect the potential impact of Vit D in the management of early stages of liver cancer.

Effects of L-ascorbic acid (C6H8O6: Vit-C) on collagen amino acids: DFT study.

Vitamin C plays a very important role in the repair of connective tissue, especially for sports whose training causes the most damage to this tissue. Therefore, many people believe that L-ascorbic acid (C6H8O6: vitamin C) reduces the recovery time between sports exercises. The most abundant form of structural protein in the body is collagen. Collagen is characterized by a high concentration of the three amino acids glycine (Gly), proline (Pro), and hydroxyproline (Hyp), which creates its characteristic triple helix structure. Therefore, in this study, the effect of vitamin C presence on the sequence, interaction, and orientation of amino acids for collagen formation is investigated using computational simulation. This study aimed to investigate the mechanism of action of vitamin C in terms of thermodynamics and structure of the reaction. The calculations are performed using density function theory (DFT) by the base set of B3LYP/6-311++G (p,d). The results show that the presence of vitamin C is effective in the formation of collagen protein for this interaction and the mechanism of amino acid sequence (Gly-Hyp-Pro) is better in the formation of collagen protein in the presence of vitamin C. The presence of Vit-C in the formation and direction of hydroxyproline (Hyp) causes its separation from the prolyl 5-hydroxylase enzyme. In the absence of vitamin C, the reaction stops at this stage and proline cannot be converted into hydroxyproline. The computational data shows vitamin C prevents unwanted interactions and directs amino acid reactions to repair connective tissue (collagen). Therefore, vitamin C acts as a cofactor in the Prolyl 5-Hydroxylase enzyme and causes it to convert proline to hydroxyl.

Cross-Parallel Transformer: Parallel ViT for Medical Image Segmentation.

Medical image segmentation primarily utilizes a hybrid model consisting of a Convolutional Neural Network and sequential Transformers. The latter leverage multi-head self-attention mechanisms to achieve comprehensive global context modelling. However, despite their success in semantic segmentation, the feature extraction process is inefficient and demands more computational resources, which hinders the network's robustness. To address this issue, this study presents two innovative methods: PTransUNet (PT model) and C-PTransUNet (C-PT model). The C-PT module refines the Vision Transformer by substituting a sequential design with a parallel one. This boosts the feature extraction capabilities of Multi-Head Self-Attention via self-correlated feature attention and channel feature interaction, while also streamlining the Feed-Forward Network to lower computational demands. On the Synapse public dataset, the PT and C-PT models demonstrate improvements in DSC accuracy by 0.87% and 3.25%, respectively, in comparison with the baseline model. As for the parameter count and FLOPs, the PT model aligns with the baseline model. In contrast, the C-PT model shows a decrease in parameter count by 29% and FLOPs by 21.4% relative to the baseline model. The proposed segmentation models in this study exhibit benefits in both accuracy and efficiency.

A Finger Vein Liveness Detection System Based on Multi-Scale Spatial-Temporal Map and Light-ViT Model.

Prosthetic attack is a problem that must be prevented in current finger vein recognition applications. To solve this problem, a finger vein liveness detection system was established in this study. The system begins by capturing short-term static finger vein videos using uniform near-infrared lighting. Subsequently, it employs Gabor filters without a direct-current (DC) component for vein area segmentation. The vein area is then divided into blocks to compute a multi-scale spatial-temporal map (MSTmap), which facilitates the extraction of coarse liveness features. Finally, these features are trained for refinement and used to predict liveness detection results with the proposed Light Vision Transformer (Light-ViT) model, which is equipped with an enhanced Light-ViT backbone, meticulously designed by interleaving multiple MN blocks and Light-ViT blocks, ensuring improved performance in the task. This architecture effectively balances the learning of local image features, controls network parameter complexity, and substantially improves the accuracy of liveness detection. The accuracy of the Light-ViT model was verified to be 99.63% on a self-made living/prosthetic finger vein video dataset. This proposed system can also be directly applied to the finger vein recognition terminal after the model is made lightweight.

Multispectral Plant Disease Detection with Vision Transformer-Convolutional Neural Network Hybrid Approaches.

Plant diseases pose a critical threat to global agricultural productivity, demanding timely detection for effective crop yield management. Traditional methods for disease identification are laborious and require specialised expertise. Leveraging cutting-edge deep learning algorithms, this study explores innovative approaches to plant disease identification, combining Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to enhance accuracy. A multispectral dataset was meticulously collected to facilitate this research using six 50 mm filter filters, covering both the visible and several near-infrared (NIR) wavelengths. Among the models employed, ViT-B16 notably achieved the highest test accuracy, precision, recall, and F1 score across all filters, with averages of 83.3%, 90.1%, 90.75%, and 89.5%, respectively. Furthermore, a comparative analysis highlights the pivotal role of balanced datasets in selecting the appropriate wavelength and deep learning model for robust disease identification. These findings promise to advance crop disease management in real-world agricultural applications and contribute to global food security. The study underscores the significance of machine learning in transforming plant disease diagnostics and encourages further research in this field.

HGR-ViT: Hand Gesture Recognition with Vision Transformer.

Hand gesture recognition (HGR) is a crucial area of research that enhances communication by overcoming language barriers and facilitating human-computer interaction. Although previous works in HGR have employed deep neural networks, they fail to encode the orientation and position of the hand in the image. To address this issue, this paper proposes HGR-ViT, a Vision Transformer (ViT) model with an attention mechanism for hand gesture recognition. Given a hand gesture image, it is first split into fixed size patches. Positional embedding is added to these embeddings to form learnable vectors that capture the positional information of the hand patches. The resulting sequence of vectors are then served as the input to a standard Transformer encoder to obtain the hand gesture representation. A multilayer perceptron head is added to the output of the encoder to classify the hand gesture to the correct class. The proposed HGR-ViT obtains an accuracy of 99.98%, 99.36% and 99.85% for the American Sign Language (ASL) dataset, ASL with Digits dataset, and National University of Singapore (NUS) hand gesture dataset, respectively.

Advancing Brain Tumor Classification through Fine-Tuned Vision Transformers: A Comparative Study of Pre-Trained Models.

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.

EfficientRMT-Net-An Efficient ResNet-50 and Vision Transformers Approach for Classifying Potato Plant Leaf Diseases.

The primary objective of this study is to develop an advanced, automated system for the early detection and classification of leaf diseases in potato plants, which are among the most cultivated vegetable crops worldwide. These diseases, notably early and late blight caused by Alternaria solani and Phytophthora infestans, significantly impact the quantity and quality of global potato production. We hypothesize that the integration of Vision Transformer (ViT) and ResNet-50 architectures in a new model, named EfficientRMT-Net, can effectively and accurately identify various potato leaf diseases. This approach aims to overcome the limitations of traditional methods, which are often labor-intensive, time-consuming, and prone to inaccuracies due to the unpredictability of disease presentation. EfficientRMT-Net leverages the CNN model for distinct feature extraction and employs depth-wise convolution (DWC) to reduce computational demands. A stage block structure is also incorporated to improve scalability and sensitive area detection, enhancing transferability across different datasets. The classification tasks are performed using a global average pooling layer and a fully connected layer. The model was trained, validated, and tested on custom datasets specifically curated for potato leaf disease detection. EfficientRMT-Net's performance was compared with other deep learning and transfer learning techniques to establish its efficacy. Preliminary results show that EfficientRMT-Net achieves an accuracy of 97.65% on a general image dataset and 99.12% on a specialized Potato leaf image dataset, outperforming existing methods. The model demonstrates a high level of proficiency in correctly classifying and identifying potato leaf diseases, even in cases of distorted samples. The EfficientRMT-Net model provides an efficient and accurate solution for classifying potato plant leaf diseases, potentially enabling farmers to enhance crop yield while optimizing resource utilization. This study confirms our hypothesis, showcasing the effectiveness of combining ViT and ResNet-50 architectures in addressing complex agricultural challenges.

CNN-ViT Supported Weakly-Supervised Video Segment Level Anomaly Detection.

Video anomaly event detection (VAED) is one of the key technologies in computer vision for smart surveillance systems. With the advent of deep learning, contemporary advances in VAED have achieved substantial success. Recently, weakly supervised VAED (WVAED) has become a popular VAED technical route of research. WVAED methods do not depend on a supplementary self-supervised substitute task, yet they can assess anomaly scores straightway. However, the performance of WVAED methods depends on pretrained feature extractors. In this paper, we first address taking advantage of two pretrained feature extractors for CNN (e.g., C3D and I3D) and ViT (e.g., CLIP), for effectively extracting discerning representations. We then consider long-range and short-range temporal dependencies and put forward video snippets of interest by leveraging our proposed temporal self-attention network (TSAN). We design a multiple instance learning (MIL)-based generalized architecture named CNN-ViT-TSAN, by using CNN- and/or ViT-extracted features and TSAN to specify a series of models for the WVAED problem. Experimental results on publicly available popular crowd datasets demonstrated the effectiveness of our CNN-ViT-TSAN.

Lymphotonic activity of Ruscus extract, hesperidin methyl chalcone and vitamin C in human lymphatic smooth muscle cells.

OBJECTIVE: Besides actions including their venotonic, anti-inflammatory, and anti-oxidant effects, venoactive drugs are expected to act on edema via their action on lymphatics. The objective of this study was to evaluate the effect of the combination of Ruscus, hesperidin methyl chalcone and Vitamin C (Ruscus/HMC/Vit C) on intracellular calcium mobilization and contraction of human lymphatic smooth muscle cells (LSMCs) to better characterize the mechanism of its lymphotonic activity. METHODS: Calcium mobilization was evidenced by videomicroscopy analysis of the fluorescence emitted by a specific Ca2+ sensitive dye and measured after injection of Ruscus/HMC/Vit C at 0.1, 0.3, 1.0, and 3.0 mg/mL into LSMCs. RESULTS: Ruscus/HMC/Vit C induced a strong and reproducible concentration-dependent calcium mobilization in LSMCs. On the contrary, another venoactive drug used as comparator, micronized purified flavonoid fraction (MPFF), did not induce calcium mobilization whatever the tested concentration. CONCLUSION: Although alternative mechanisms of action may result in potential lymphotonic effects, the efficacy of lymphotonic products is nonetheless related to their stimulating effect on the contractile activity of the smooth muscle cells surrounding lymphatic vessels. In the light of the results obtained in this study, the direct effect of Ruscus/HMC/Vit C on LSMC contraction may partially explain its clinical efficacy on lymphotonic activity, as has been observed in terms of objective signs of edema as reported in the recent guidelines on chronic venous disease.

A randomized clinical trial to assess the effect of zinc and vitamin D supplementation in addition to hypertonic saline on treatment of acute bronchiolitis.

BACKGROUND: Bronchiolitis, the most common cause of hospitalization in infancy has not yet a definitive treatment. This study was conducted to assess the effect of Zinc and vitamin D on treatment of infants with bronchiolitis. METHODS: In this double blind, randomized clinical trial, 94 infants aged 2 to 23 months, admitted in Mousavi Hospital in Zanjan, Iran, with the diagnosis of acute bronchiolitis were randomly assigned into 3 groups. The control group was only treated with hypertonic saline. The two case groups received either 100 unit/kg/day of Vitamin D or 20 mg/day of zinc in addition to hypertonic saline. Wheezing, duration of hospital stay, cough, cyanosis, respiratory distress and the respiratory rate in the first, third and seventh day of hospitalization were evaluated. RESULTS: There was no significant difference between groups in terms of age, sex, weight, passive smoking, wheezing, oxygen saturation, cyanosis and type of delivery. On the third day of hospitalization, the respiratory rate/min in the control group, the groups receiving vitamin D and zinc were 45.2 ± 10.7, 37.8 ± 3.9 and 41.1 ± 9.1 respectively and the result of repeated measure analysis didn't show any significant difference between the 3 groups (P = 0.562). Duration of hospitalization in the group receiving Vitamin D or zinc and in controls were 4.2 ± 2.6, 4.4 ± 2.2 and 5.1 ± 2.4 days respectively and this difference was not significant. Zinc receiving patients did not differ from the control group regarding to respiratory rate, cyanosis and wheezing. CONCLUSION: Vitamin D or zinc administration was not effective in reducing respiratory rate in children with bronchiolitis. Trial registration This project was approved by the Institutional Ethics Committee (IR, ZUMS.REC.1396.50), and registered on IRCT (IRCT20131217015835N7).

Pharmacological role of Vitamin C in stress-induced cardiac dysfunction via alteration in Gut microbiota.

There is emerging evidence exhibiting the strong association of gut microbiota with cardiovascular metabolic functions. Cardiac diseases may alter the richness, diversity, and composition of the gut microbiome. Vitamin C (Vit C) plays an important role in many metabolic activities in cardiovascular diseases. In this study, we induced cardiac remodeling by the forced swim stress model in rats, which resulted in dysbiosis. Adult male Wistar rats were designated into the following groups: (i) normal control (NC), (ii) forced swim induced stress (FSIS) control, (iii) FSIS + Vit C treatment, and (iv) Vit C control. Stool samples were collected for estimation for 90 days, and at the end of the study, the animals were killed and heart tissue was isolated for histochemical analysis. We observed a sharp fall in the operational taxonomic unit in the FSIS control animals as compared to NC animals. Treatment with Vit C exhibited a decrease in Bacteroidetes while raising the abundance of spirochetes. Plasma levels of creatine kinase myocardial band (CKMB) in the treatment group reduced to 175.7 ± 3.41 U/L, from 317.7 ± 34.48 U/L in the diabetic control group. Also, the C-reactive protein level in the disease control group was 18 ± 0.93 mg/dl, which reduced to the normal level of 7.53 ± 0.20 mg/dl on treatment with Vit C administration. Our results suggest that FSIS induced cardiac complication is also associated with changes in gut microbial abundance. Higher doses of Vit C, which strengthens the immunity, have shown some positive outcomes on cardiac complications. The abundance of gut microbiota is also associated with the immune system, which in turn marks the impact of a disease. More the richness and diversity of the gut microbiome, healthier is the composition that can withstand the external threats of disease and other major challenges in the environment. Hence microbiome abundance plays an important role in the therapies or future prospects of disease. Histopathological studies support the serological and microbiome examination and warrant the cardioprotective influence of Vit C in the stress-induced cardiac dysfunction model.

Gait-ViT: Gait Recognition with Vision Transformer.

Identifying an individual based on their physical/behavioral characteristics is known as biometric recognition. Gait is one of the most reliable biometrics due to its advantages, such as being perceivable at a long distance and difficult to replicate. The existing works mostly leverage Convolutional Neural Networks for gait recognition. The Convolutional Neural Networks perform well in image recognition tasks; however, they lack the attention mechanism to emphasize more on the significant regions of the image. The attention mechanism encodes information in the image patches, which facilitates the model to learn the substantial features in the specific regions. In light of this, this work employs the Vision Transformer (ViT) with an attention mechanism for gait recognition, referred to as Gait-ViT. In the proposed Gait-ViT, the gait energy image is first obtained by averaging the series of images over the gait cycle. The images are then split into patches and transformed into sequences by flattening and patch embedding. Position embedding, along with patch embedding, are applied on the sequence of patches to restore the positional information of the patches. Subsequently, the sequence of vectors is fed to the Transformer encoder to produce the final gait representation. As for the classification, the first element of the sequence is sent to the multi-layer perceptron to predict the class label. The proposed method obtained 99.93% on CASIA-B, 100% on OU-ISIR D and 99.51% on OU-LP, which exhibit the ability of the Vision Transformer model to outperform the state-of-the-art methods.

Venom Collection by Electrical Stimulation in the Invasive Species Polistes dominula Reared Using a Vespiculture Regime.

Specific Venom Immunotherapy (VIT) is practiced with venom extracted from insects, and is the specific therapy used for patients highly allergic to social insect (Hymenoptera) stings. Due to the dramatic shortage of vespid species in the local environment, we coupled vespiculture techniques of Polistes paper wasps with a venom collection procedure based on the electrical stimulation of individuals from entire colonies. The procedure involves little to no disturbance of the individual insects, and at the same time, successfully allows for the extraction of venom containing all allergens necessary for VIT.

Vitamin C: historical perspectives and heart failure.

Vitamin C (Vit C) is an ideal antioxidant as it is easily available, water soluble, very potent, least toxic, regenerates other antioxidants particularly Vit E, and acts as a cofactor for different enzymes. It has received much attention due to its ability in limiting reactive oxygen species, oxidative stress, and nitrosative stress, as well as it helps to maintain some of the normal metabolic functions of the cell. However, over 140 clinical trials using Vit C in different pathological conditions such as myocardial infarction, gastritis, diabetes, hypertension, stroke, and cancer have yielded inconsistent results. Such a divergence calls for new strategies to establish practical significance of Vit C in heart failure or even in its prevention. For a better understanding of Vit C functioning, it is important to revisit its transport across the cell membrane and subcellular interactions. In this review, we have highlighted some historical details of Vit C and its transporters in the heart with a particular focus on heart failure in cancer chemotherapy.