Multimodal deep learning for liver cancer applications: a scoping review.

Background: Hepatocellular carcinoma is a malignant neoplasm of the liver and a leading cause of cancer-related deaths worldwide. The multimodal data combines several modalities, such as medical images, clinical parameters, and electronic health record (EHR) reports, from diverse sources to accomplish the diagnosis of liver cancer. The introduction of deep learning models with multimodal data can enhance the diagnosis and improve physicians' decision-making for cancer patients. Objective: This scoping review explores the use of multimodal deep learning techniques (i.e., combining medical images and EHR data) in diagnosing and prognosis of hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA). Methodology: A comprehensive literature search was conducted in six databases along with forward and backward references list checking of the included studies. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping review guidelines were followed for the study selection process. The data was extracted and synthesized from the included studies through thematic analysis. Results: Ten studies were included in this review. These studies utilized multimodal deep learning to predict and diagnose hepatocellular carcinoma (HCC), but no studies examined cholangiocarcinoma (CCA). Four imaging modalities (CT, MRI, WSI, and DSA) and 51 unique EHR records (clinical parameters and biomarkers) were used in these studies. The most frequently used medical imaging modalities were CT scans followed by MRI, whereas the most common EHR parameters used were age, gender, alpha-fetoprotein AFP, albumin, coagulation factors, and bilirubin. Ten unique deep-learning techniques were applied to both EHR modalities and imaging modalities for two main purposes, prediction and diagnosis. Conclusion: The use of multimodal data and deep learning techniques can help in the diagnosis and prediction of HCC. However, there is a limited number of works and available datasets for liver cancer, thus limiting the overall advancements of AI for liver cancer applications. Hence, more research should be undertaken to explore further the potential of multimodal deep learning in liver cancer applications.

Artificial Intelligence-Based Methods for Integrating Local and Global Features for Brain Cancer Imaging: Scoping Review.

BACKGROUND: Transformer-based models are gaining popularity in medical imaging and cancer imaging applications. Many recent studies have demonstrated the use of transformer-based models for brain cancer imaging applications such as diagnosis and tumor segmentation. OBJECTIVE: This study aims to review how different vision transformers (ViTs) contributed to advancing brain cancer diagnosis and tumor segmentation using brain image data. This study examines the different architectures developed for enhancing the task of brain tumor segmentation. Furthermore, it explores how the ViT-based models augmented the performance of convolutional neural networks for brain cancer imaging. METHODS: This review performed the study search and study selection following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The search comprised 4 popular scientific databases: PubMed, Scopus, IEEE Xplore, and Google Scholar. The search terms were formulated to cover the interventions (ie, ViTs) and the target application (ie, brain cancer imaging). The title and abstract for study selection were performed by 2 reviewers independently and validated by a third reviewer. Data extraction was performed by 2 reviewers and validated by a third reviewer. Finally, the data were synthesized using a narrative approach. RESULTS: Of the 736 retrieved studies, 22 (3%) were included in this review. These studies were published in 2021 and 2022. The most commonly addressed task in these studies was tumor segmentation using ViTs. No study reported early detection of brain cancer. Among the different ViT architectures, Shifted Window transformer-based architectures have recently become the most popular choice of the research community. Among the included architectures, UNet transformer and TransUNet had the highest number of parameters and thus needed a cluster of as many as 8 graphics processing units for model training. The brain tumor segmentation challenge data set was the most popular data set used in the included studies. ViT was used in different combinations with convolutional neural networks to capture both the global and local context of the input brain imaging data. CONCLUSIONS: It can be argued that the computational complexity of transformer architectures is a bottleneck in advancing the field and enabling clinical transformations. This review provides the current state of knowledge on the topic, and the findings of this review will be helpful for researchers in the field of medical artificial intelligence and its applications in brain cancer.

Improving diagnosis and prognosis of lung cancer using vision transformers: a scoping review.

BACKGROUND: Vision transformer-based methods are advancing the field of medical artificial intelligence and cancer imaging, including lung cancer applications. Recently, many researchers have developed vision transformer-based AI methods for lung cancer diagnosis and prognosis. OBJECTIVE: This scoping review aims to identify the recent developments on vision transformer-based AI methods for lung cancer imaging applications. It provides key insights into how vision transformers complemented the performance of AI and deep learning methods for lung cancer. Furthermore, the review also identifies the datasets that contributed to advancing the field. METHODS: In this review, we searched Pubmed, Scopus, IEEEXplore, and Google Scholar online databases. The search terms included intervention terms (vision transformers) and the task (i.e., lung cancer, adenocarcinoma, etc.). Two reviewers independently screened the title and abstract to select relevant studies and performed the data extraction. A third reviewer was consulted to validate the inclusion and exclusion. Finally, the narrative approach was used to synthesize the data. RESULTS: Of the 314 retrieved studies, this review included 34 studies published from 2020 to 2022. The most commonly addressed task in these studies was the classification of lung cancer types, such as lung squamous cell carcinoma versus lung adenocarcinoma, and identifying benign versus malignant pulmonary nodules. Other applications included survival prediction of lung cancer patients and segmentation of lungs. The studies lacked clear strategies for clinical transformation. SWIN transformer was a popular choice of the researchers; however, many other architectures were also reported where vision transformer was combined with convolutional neural networks or UNet model. Researchers have used the publicly available lung cancer datasets of the lung imaging database consortium and the cancer genome atlas. One study used a cluster of 48 GPUs, while other studies used one, two, or four GPUs. CONCLUSION: It can be concluded that vision transformer-based models are increasingly in popularity for developing AI methods for lung cancer applications. However, their computational complexity and clinical relevance are important factors to be considered for future research work. This review provides valuable insights for researchers in the field of AI and healthcare to advance the state-of-the-art in lung cancer diagnosis and prognosis. We provide an interactive dashboard on lung-cancer.onrender.com/ .

Identifying the role of vision transformer for skin cancer-A scoping review.

Introduction: Detecting and accurately diagnosing early melanocytic lesions is challenging due to extensive intra- and inter-observer variabilities. Dermoscopy images are widely used to identify and study skin cancer, but the blurred boundaries between lesions and besieging tissues can lead to incorrect identification. Artificial Intelligence (AI) models, including vision transformers, have been proposed as a solution, but variations in symptoms and underlying effects hinder their performance. Objective: This scoping review synthesizes and analyzes the literature that uses vision transformers for skin lesion detection. Methods: The review follows the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Revise) guidelines. The review searched online repositories such as IEEE Xplore, Scopus, Google Scholar, and PubMed to retrieve relevant articles. After screening and pre-processing, 28 studies that fulfilled the inclusion criteria were included. Results and discussions: The review found that the use of vision transformers for skin cancer detection has rapidly increased from 2020 to 2022 and has shown outstanding performance for skin cancer detection using dermoscopy images. Along with highlighting intrinsic visual ambiguities, irregular skin lesion shapes, and many other unwanted challenges, the review also discusses the key problems that obfuscate the trustworthiness of vision transformers in skin cancer diagnosis. This review provides new insights for practitioners and researchers to understand the current state of knowledge in this specialized research domain and outlines the best segmentation techniques to identify accurate lesion boundaries and perform melanoma diagnosis. These findings will ultimately assist practitioners and researchers in making more authentic decisions promptly.

Enhanced Thermal Stability and Synergistic Effects of Magnesium and Iron Borate Composites against Pathogenic Bacteria.

A simple process based on the dual roles of both magnesium oxide (MgO) and iron oxide (FeO) with boron (B) as precursors and catalysts has been developed for the synthesis of borate composites of magnesium and iron (Mg2B2O5-Fe3BO6) at 1200°C. The as-synthesized composites can be a single material with the improved and collective properties of both iron borates (Fe3BO6) and magnesium borates (Mg2B2O5). At higher temperatures, the synthesized Mg2B2O5-Fe3BO6 composite is found thermally more stable than the single borates of both magnesium and iron. Similarly, the synthesized composites are found to prevent the growth of both gram-positive (Staphylococcus aureus) and gram-negative (Escherichia coli) pathogenic bacteria on all the tested concentrations. Moreover, the inhibitory effect of the synthesized composite increases with an increase in concentration and is more pronounced against S. aureus as compared to E. coli.

A new generative adversarial network for medical images super resolution.

For medical image analysis, there is always an immense need for rich details in an image. Typically, the diagnosis will be served best if the fine details in the image are retained and the image is available in high resolution. In medical imaging, acquiring high-resolution images is challenging and costly as it requires sophisticated and expensive instruments, trained human resources, and often causes operation delays. Deep learning based super resolution techniques can help us to extract rich details from a low-resolution image acquired using the existing devices. In this paper, we propose a new Generative Adversarial Network (GAN) based architecture for medical images, which maps low-resolution medical images to high-resolution images. The proposed architecture is divided into three steps. In the first step, we use a multi-path architecture to extract shallow features on multiple scales instead of single scale. In the second step, we use a ResNet34 architecture to extract deep features and upscale the features map by a factor of two. In the third step, we extract features of the upscaled version of the image using a residual connection-based mini-CNN and again upscale the feature map by a factor of two. The progressive upscaling overcomes the limitation for previous methods in generating true colors. Finally, we use a reconstruction convolutional layer to map back the upscaled features to a high-resolution image. Our addition of an extra loss term helps in overcoming large errors, thus, generating more realistic and smooth images. We evaluate the proposed architecture on four different medical image modalities: (1) the DRIVE and STARE datasets of retinal fundoscopy images, (2) the BraTS dataset of brain MRI, (3) the ISIC skin cancer dataset of dermoscopy images, and (4) the CAMUS dataset of cardiac ultrasound images. The proposed architecture achieves superior accuracy compared to other state-of-the-art super-resolution architectures.

The role of generative adversarial networks in brain MRI: a scoping review.

The performance of artificial intelligence (AI) for brain MRI can improve if enough data are made available. Generative adversarial networks (GANs) showed a lot of potential to generate synthetic MRI data that can capture the distribution of real MRI. Besides, GANs are also popular for segmentation, noise removal, and super-resolution of brain MRI images. This scoping review aims to explore how GANs methods are being used on brain MRI data, as reported in the literature. The review describes the different applications of GANs for brain MRI, presents the most commonly used GANs architectures, and summarizes the publicly available brain MRI datasets for advancing the research and development of GANs-based approaches. This review followed the guidelines of PRISMA-ScR to perform the study search and selection. The search was conducted on five popular scientific databases. The screening and selection of studies were performed by two independent reviewers, followed by validation by a third reviewer. Finally, the data were synthesized using a narrative approach. This review included 139 studies out of 789 search results. The most common use case of GANs was the synthesis of brain MRI images for data augmentation. GANs were also used to segment brain tumors and translate healthy images to diseased images or CT to MRI and vice versa. The included studies showed that GANs could enhance the performance of AI methods used on brain MRI imaging data. However, more efforts are needed to transform the GANs-based methods in clinical applications.

Exploring biocontrol and growth-promoting potential of multifaceted PGPR isolated from natural suppressive soil against the causal agent of chickpea wilt.

Chickpea is an important nutritive food crop both for humans and animals. Chickpea wilt caused by Fusarium oxysporum f.sp. ciceris (Foc) results in huge yield losses every year. Chickpea being a food crop requires the development of an eco-friendly bio-pesticide to effectively control the chickpea wilt disease. In this study, more than 50 bacterial stains isolated from the rhizosphere of healthy plants growing in wilt sick soil were examined for their Foc antagonist activities. Out of these, 17 strains showing > 90% growth inhibition of Foc were then characterized for their plant growth-promoting (PGP) and biocontrol traits. The biocontrol and PGP traits identified include amylase, hydrogen cyanide, protease, cellulase, chitinase activities, p-solubilization, nitrogen-fixing, and indole-3-acetic acid production. Two bacterial strains, IR-27 and IR-57, exhibiting the highest Foc proliferation inhibition and the PGP potential along with a consortium of four different strains (Serratia sp. IN-1, Serratia sp. IS-1, Enterobacter sp. IN-2, Enterobacter sp. IN-6) were used for controlling the chickpea wilt disease and growth promotion of the chickpea plants. Confocal laser scanning microscopy revealed their root colonization ability with partial or complete elimination of broken Foc mycelia and hyphae from roots. The bacterial inoculations particularly the consortium significantly suppressed the disease and improved the overall root morphology traits (root length, root surface area, root volume, forks, tips, and crossings), resulting in enhanced growth of the chickpea plants. Significant changes in growth (107% increase in root length, 23% increase in shoot length, and 54% increase in branches) in Foc-challenged plants were observed when inoculated with the consortium. Further investigations revealed that the chickpea plants inoculated with bacterial strains induced the expression of a number of key defence enzymes, including the phenylalanine ammonia lyase, peroxidase, polyphenol peroxidase, ß-1,3 glucanase, which might have helped the plants to thwart the pathogen attack. These findings indicate the potential of our identified bacterial strains to be used as a natural biopesticide for controlling the chickpea wilt disease.

Recent Developments in Artificial Intelligence-Based Techniques for Prostate Cancer Detection: A Scoping Review.

Artificial intelligence (AI) techniques can contribute to the early diagnosis of prostate cancer. Recently, there has been a sharp increase in the literature on AI techniques for prostate cancer diagnosis. This review article presents a summary of the AI methods that detect and diagnose prostate cancer using different medical imaging modalities. Following the PRISMA-ScR principle, this review covers 69 studies selected from 1441 searched papers published in the last three years. The application of AI methods reported in these articles can be divided into three broad categories: diagnosis, grading, and segmentation of tissues that have prostate cancer. Most of the AI methods leveraged convolutional neural networks (CNNs) due to their ability to extract complex features. Some studies also reported traditional machine learning methods, such as support vector machines (SVM), decision trees for classification, LASSO, and Ridge regression methods for features extraction. We believe that the implementation of AI-based tools will support clinicians to provide better diagnosis plans for prostate cancer.

Synthesis of enriched boron nitride nanocrystals: A potential element for biomedical applications.

The shortcomings in Boron neutron capture therapy (BNCT) and Hyperthermia for killing the tumor cell desired for the synthesis of a new kind of material suitable to be first used in BNCT and later on enable the conditions for Hyperthermia to destroy the tumor cell. The desire led to the synthesis of large band gap semiconductor nano-size Boron-10 enriched crystals of hexagonal boron nitride (10BNNCs). The contents of 10BNNCs are analyzed with the help of x-ray photoelectron spectroscopy (XPS) and counter checked with Raman and XRD. The 10B-contents in 10BNNCs produce 7Li and 4He nuclei. A Part of the 7Li and 4He particles released in the cell is allowed to kill the tumor (via BNCT) whereas the rest produce electron-hole pairs in the semiconductor layer of 10BNNCs suggested to work in Hyperthermia with an externally applied field.

A fungal based synthesis method for copper nanoparticles with the determination of anticancer, antidiabetic and antibacterial activities.

BACKGROUND: Biological procedures for the synthesis of copper nanoparticles (CuNPs) are eco-friendly, cost-effective, and easily scalable as compared to chemical and physical methods. METHODS: In the present study, a simple fungus based synthesis method was used for copper nanoparticles. After morphological and molecular characterization of fungal strains, Aspergillus niger strain STA9 was used for CuNPs synthesis. Particles synthesized by fungi were characterized by UV-visible spectrophotometer, FTIR, and Zetasizer. The MTT anti-cancer, anti-diabetic and antibacterial assays of CuNPs were performed. RESULTS: The CuNPs were produced at optimized conditions with a size of 500 nm, Z-average 398.2 nm, and 0.246 poly dispersion index. These particles were quantified at 480 nm and FTIR confirmed the existence of OH and -C=C- functional groups. MTT assay revealed that CuNPs have a significant cytotoxic effect against human hepatocellular carcinoma cell lines (Huh-7) with 3.09 µg/ml IC50 value. Alpha-glucosidase inhibition showed that CuNPs have a moderate antidiabetic effect. The agar well antibacterial effect indicated 19, 21, 16, 20, and 17 mm zone of inhibition against Escherichia coli, Staphylococcus aureus, Klebsiella pneumoniae, Micrococcus luteus and Bacillus subtilis respectively. CONCLUSION: Such biomedical applications of CuNPs reveal the importance of a targeted drug delivery system.

A novel homozygous ARL13B variant in patients with Joubert syndrome impairs its guanine nucleotide-exchange factor activity.

ARL13B encodes for the ADP-ribosylation factor-like 13B GTPase, which is required for normal cilia structure and Sonic hedgehog (Shh) signaling. Disruptions in cilia structure or function lead to a class of human disorders called ciliopathies. Joubert syndrome is characterized by a wide spectrum of symptoms, including a variable degree of intellectual disability, ataxia, and ocular abnormalities. Here we report a novel homozygous missense variant c.[223G>A] (p.(Gly75Arg) in the ARL13B gene, which was identified by whole-exome sequencing of a trio from a consanguineous family with multiple-affected individuals suffering from intellectual disability, ataxia, ocular defects, and epilepsy. The same variant was also identified in a second family. We saw a striking difference in the severity of ataxia between affected male and female individuals in both families. Both ARL13B and ARL13B-c.[223G>A] (p.(Gly75Arg) expression rescued the cilia length and Shh defects displayed by Arl13b hennin (null) cells, indicating that the variant did not disrupt either ARL13B function. In contrast, ARL13B-c.[223G>A] (p.(Gly75Arg) displayed a marked loss of ARL3 guanine nucleotide-exchange factor activity, with retention of its GTPase activities, highlighting the correlation between its loss of function as an ARL3 guanine nucleotide-exchange factor and Joubert syndrome.

Novel key metabolites reveal further branching of the roquefortine/meleagrin biosynthetic pathway.

Metabolic profiling and structural elucidation of novel secondary metabolites obtained from derived deletion strains of the filamentous fungus Penicillium chrysogenum were used to reassign various previously ascribed synthetase genes of the roquefortine/meleagrin pathway to their corresponding products. Next to the structural characterization of roquefortine F and neoxaline, which are for the first time reported for P. chrysogenum, we identified the novel metabolite roquefortine L, including its degradation products, harboring remarkable chemical structures. Their biosynthesis is discussed, questioning the exclusive role of glandicoline A as key intermediate in the pathway. The results reveal that further enzymes of this pathway are rather unspecific and catalyze more than one reaction, leading to excessive branching in the pathway with meleagrin and neoxaline as end products of two branches.

Phytoremediation of heavy metals--concepts and applications.

The mobilization of heavy metals by man through extraction from ores and processing for different applications has led to the release of these elements into the environment. Since heavy metals are nonbiodegradable, they accumulate in the environment and subsequently contaminate the food chain. This contamination poses a risk to environmental and human health. Some heavy metals are carcinogenic, mutagenic, teratogenic and endocrine disruptors while others cause neurological and behavioral changes especially in children. Thus remediation of heavy metal pollution deserves due attention. Different physical and chemical methods used for this purpose suffer from serious limitations like high cost, intensive labor, alteration of soil properties and disturbance of soil native microflora. In contrast, phytoremediation is a better solution to the problem. Phytoremediation is the use of plants and associated soil microbes to reduce the concentrations or toxic effects of contaminants in the environments. It is a relatively recent technology and is perceived as cost-effective, efficient, novel, eco-friendly, and solar-driven technology with good public acceptance. Phytoremediation is an area of active current research. New efficient metal hyperaccumulators are being explored for applications in phytoremediation and phytomining. Molecular tools are being used to better understand the mechanisms of metal uptake, translocation, sequestration and tolerance in plants. This review article comprehensively discusses the background, concepts and future trends in phytoremediation of heavy metals.