Carbon dots (CDs) are a new category of crystalline, quasi-spherical fluorescence, “zero-dimensional” carbon nanomaterials with a spatial size between 1 nm to 10 nm and have gained widespread attention in recent years. Green CDs are carbon dots synthesised from renewable biomass such as agro-waste, plants or medicinal plants and other organic biomaterials. Plant-mediated synthesis of CDs is a green chemistry approach that connects nanotechnology with the green synthesis of CDs. Notably, CDs made with green technology are economical and far superior to those manufactured with physicochemical methods due to their exclusive benefits, such as being affordable, having high stability, having a simple protocol, and being safer and eco-benign. Green CDs can be synthesized by using ultrasonic strategy, chemical oxidation, carbonization, solvothermal and hydrothermal processes, and microwave irradiation using various plant-based organic resources. CDs made by green technology have diverse applications in biomedical fields such as bioimaging, biosensing and nanomedicine, which are ascribed to their unique properties, including excellent luminescence effect, strong stability and good biocompatibility. This review mainly focuses on green CDs synthesis, characterization techniques, beneficial properties of plant resource-based green CDs and their biomedical applications. This review article also looks at the research gaps and future research directions for the continuous deepening of the exploration of green CDs.

Medical imaging is a complex process that capitulates images created by X-rays, ultrasound imaging, angiography, etc. During the imaging process, it also captures image noise during image acquisition, some of which are extremely corrosive, creating a disturbance that results in image degradation. The proposed work addresses the challenge to eliminate the corrosive Gaussian additive white noise from computed tomography (CT) images while preserving the fine details. The proposed approach is synthesized by amalgamating the concept of method noise with a deep learning-based framework of a convolutional neural network (CNN). The corrupted images are obtained by explicit addition of Gaussian additive white noise at multiple noise variance levels (σ = 10, 15, 20, 25). The denoised images obtained are then evaluated according to their visual quality and quantitative metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). These metrics for denoised CT images are then compared with their respective values for the reference CT image. The average PSNR value of the proposed method is 25.82, the average SSIM value is 0.85, and the average computational time is 2.8760. To better understand the proposed approach’s effectiveness, an intensity profile of denoised and original medical images is plotted and compared. To further test the performance of the proposed methodology, the results obtained are also compared with that of other non-traditional methods. The critical analysis of the results shows the commendable efficiency of the proposed methodology in denoising the medical CT images corrupted by Gaussian noise. This approach can be utilized in multiple pragmatic areas of application in the field of medical image processing.

Background and Objectives: Brain Tumor Fusion-based Segments and Classification-Non-enhancing tumor (BTFSC-Net) is a hybrid system for classifying brain tumors that combine medical image fusion, segmentation, feature extraction, and classification procedures. Materials and Methods: to reduce noise from medical images, the hybrid probabilistic wiener filter (HPWF) is first applied as a preprocessing step. Then, to combine robust edge analysis (REA) properties in magnetic resonance imaging (MRI) and computed tomography (CT) medical images, a fusion network based on deep learning convolutional neural networks (DLCNN) is developed. Here, the brain images’ slopes and borders are detected using REA. To separate the sick region from the color image, adaptive fuzzy c-means integrated k-means (HFCMIK) clustering is then implemented. To extract hybrid features from the fused image, low-level features based on the redundant discrete wavelet transform (RDWT), empirical color features, and texture characteristics based on the gray-level cooccurrence matrix (GLCM) are also used. Finally, to distinguish between benign and malignant tumors, a deep learning probabilistic neural network (DLPNN) is deployed. Results: according to the findings, the suggested BTFSC-Net model performed better than more traditional preprocessing, fusion, segmentation, and classification techniques. Additionally, 99.21% segmentation accuracy and 99.46% classification accuracy were reached using the proposed BTFSC-Net model. Conclusions: earlier approaches have not performed as well as our presented method for image fusion, segmentation, feature extraction, classification operations, and brain tumor classification. These results illustrate that the designed approach performed more effectively in terms of enhanced quantitative evaluation with better accuracy as well as visual performance.

Computed tomography (CT) is used in medical applications to produce digital medical imaging of the human body and is acquired by the reconstruction process, where X-rays are the key component of CT imaging. The present coronavirus outbreak has spawned new medical device and technology research fields. COVID-19 most severely affects people with poor immunity; children and pregnant women are more susceptible. A CT scan will be required to assess the infection’s severity. As a result, to reduce the radiation levels significantly there is a need to minimize the CT scan noise. The quality of CT images may degrade in the form of noisy images due to low radiation levels. Hence, this study proposes a novel denoising methodology for COVID-19 CT images with a low dose, where a convolution neural network (CNN) and batch normalization were utilized for denoising. From different output metrics such as peak signal-to-noise ratio (PSNR) and image quality index (IQI), the accuracy of the resulting CT images was checked and evaluated, where IQI obtained the best results in terms of 99% accuracy. The findings were also compared with the outcomes of related recent research in the domain. After a detailed review of the findings, it was noted that the proposed algorithm in the present study performed better in comparision to the existing literature.

Background: Cancer is one of the leading causes of an increasing number of deaths in modern society. As the population increases, there is an increased thrust for screening newer anticancer (phytoconstituents) agents to manage cancers. Around 35000 herbal phytoconstituents are obtained from plants, animals and marine sources to create awareness of green therapy in managing, reducing, and minimizing side effects of modern chemotherapeutics and radiation therapy. The herbal plants are the richest sources of natural remedies and bioactive compounds that promote medicines' alternative systems as a green approach for managing various cancers. The terpenoids, saponins, volatile oils, and flavonoid phytoconstituents are most efficiently used to manage cancer with minimal side effects. Objective: The objectives of the present study are to investigate the efficacious, potent and safe use of herbal phytoconstituents extracts in the management of cancers and study their mechanism of action through alteration of transcription proteins, blocking G-2/M phase, distortion of tubulin structure, generation of reactive oxygen species, lipid peroxidation, cell cycle arrest, and anti-proliferation induced cell apoptosis for target specific cancer treatment. The information was collected from databases such as ScienceDirect, PubMed, Google Scholar, Academia, MedLine, and WoS. Methods: The literature was surveyed, and keywords like cancer therapeutics, metastasis, proliferation, cell apoptosis, cell lines, phytoconstituents for cancer management, and related disorders were screened. Results: The findings suggested that the crude extracts act as an antioxidant, free radical scavenger, or anti-aging agent exploited in the management of cancers along with treatment of other infectious diseases like ulcers, gout, liver diseases, respiratory tract infection, renal disorders, blood disorders, CVD, anti-inflammatory and several wound infections. Conclusion: The phytoactive moieties having herbal extracts help improve the compromised immunity status of affected patients and provide measures for scientific studies of newer anticancer agents in herbal industries.

Irrespective of medical technology improvements, cancer ranks among the leading causes of mortality worldwide. Although numerous cures and treatments exist, creating alternative cancer therapies with fewer adverse side effects is vital. Since ancient times, plant bioactive compounds have already been used as a remedy to heal cancer. These plant bioactive compounds and their anticancer activity can also deregulate the microRNAs (miRNAs) in the cancerous cells. Therefore, the deregulation of miRNAs in cancer cells by plant bioactive compounds and the usage of the related miRNA could be a promising approach for cancer cure, mainly to prevent cancer and overcome chemotherapeutic side effect problems. Hence, this review highlights the function of plant bioactive compounds as an anticancer agent through the underlying mechanism that alters the miRNA expression in cancer cells, ultimately leading to apoptosis. Moreover, this review provides insight into using plant bioactive compounds -driven miRNAs as an anticancer agent to develop miRNA-based cancer gene therapy. They can be the potential resource for gene therapy and novel strategies targeting cancer therapeutics.