Deciphering the Antibiofilm, Antibacterial, and Antioxidant Potential of Essential Oil from Indian Garlic and its Phytocompounds Against Foodborne Pathogens.

Garlic (Allium sativum L.), particularly its volatile essential oil, is widely recognized for medicinal properties. We have evaluated the efficacy of Indian Garlic Essential Oil (GEO) for antimicrobial and antibiofilm activity and its bioactive constituents. Allyl sulfur-rich compounds were identified as predominant phytochemicals in GEO, constituting 96.51% of total volatile oils, with 38% Diallyl trisulphide (DTS) as most abundant. GEO exhibited significant antibacterial activity against eleven bacteria, including three drug-resistant strains with minimum inhibitory concentrations (MICs) ranging from 78 to 1250 µg/mL. In bacterial growth kinetic assay GEO effectively inhibited growth of all tested strains at its ½ MIC. Antibiofilm activity was evident against two important human pathogens, S. aureus and P. aeruginosa. Mechanistic studies demonstrated that GEO disrupts bacterial cell membranes, leading to the release of nucleic acids, proteins, and reactive oxygen species. Additionally, GEO demonstrated potent antioxidant activity at IC50 31.18 mg/mL, while its isolated constituents, Diallyl disulphide (DDS) and Diallyl trisulphide (DTS), showed effective antibacterial activity ranging from 125 to 500 µg/mL and 250-1000 µg/mL respectively. Overall, GEO displayed promising antimicrobial and antibiofilm activity against enteric bacteria, suggesting its potential application in the food industry.

GC/MS Profiling and Evaluation of Leaf Essential Oil for Bactericidal Effect and Free Radical Scavenging Activity of Plectranthus amboinicus (Lour.) Spreng Collected from Odisha, India.

Plectranthus amboinicus (Lour.) Spreng, known as the Indian borage or Mexican mint, is one of the most documented species in the family Lamiaceae for its therapeutic and pharmaceutical values. It is found in the tropical and subtropical regions of the world. The leaf essential oil has immense medicinal benefits like treating illnesses of the skin and disorders like colds, asthma, constipation, headaches, coughs, and fevers. After analyzing earlier reports with regard to the quantity and quality of leaf oil yield, we discovered that the germplasm taken from Odisha is preferable to other germplasms. The objective of the present work is to evaluate the free radical scavenging activity and bactericidal effect of leaf essential oil (EO) of Plectranthus amboinicus (Lour.) Spreng collected from the state of Odisha, India. The hydro distillation technique has been used for essential oil extraction. Upon GC/MS analysis, approximately 57 compounds were identified with Carvacrol as the major compound (peak area=20.25 %), followed by p-thymol (peak area=20.17 %), o-cymene (peak area=19.41 %) and carene (peak area=15.89 %). On evaluation of free radical scavenging activity, it was recorded that the best value of inhibitory concentration, was for DPPH with IC50 =18.64 ppm and for H2 O2 with IC50 =9.35 ppm. The EO showed efficient bactericidal effect against both gram positive (Mycobacterium smegmatis, Staphylococcus aureus, Enterococcus faecium) and gram negative (Escherichia coli, Vibrio cholerae, Klebsiella pneumoniae) bacteria studied through well diffusion method. Fumigatory action of the essential oil was found against M. smegmatis, the model organism for tuberculosis study. Alamar Blue assay, gave a result with MIC value for M. smegmatis i. e., 0.12 µg/ml and the MBC value of 0.12 µg/ml. Hence, P. amboinicus found in Odisha can be suggested as an elite variety and should be further investigated for efficient administration in drug formulation.

AI-driven estimation of O6 methylguanine-DNA-methyltransferase (MGMT) promoter methylation in glioblastoma patients: a systematic review with bias analysis.

BACKGROUND: Accurate and non-invasive estimation of MGMT promoter methylation status in glioblastoma (GBM) patients is of paramount clinical importance, as it is a predictive biomarker associated with improved overall survival (OS). In response to the clinical need, recent studies have focused on the development of non-invasive artificial intelligence (AI)-based methods for MGMT estimation. In this systematic review, we not only delve into the technical aspects of these AI-driven MGMT estimation methods but also emphasize their profound clinical implications. Specifically, we explore the potential impact of accurate non-invasive MGMT estimation on GBM patient care and treatment decisions. METHODS: Employing a PRISMA search strategy, we identified 33 relevant studies from reputable databases, including PubMed, ScienceDirect, Google Scholar, and IEEE Explore. These studies were comprehensively assessed using 21 diverse attributes, encompassing factors such as types of imaging modalities, machine learning (ML) methods, and cohort sizes, with clear rationales for attribute scoring. Subsequently, we ranked these studies and established a cutoff value to categorize them into low-bias and high-bias groups. RESULTS: By analyzing the 'cumulative plot of mean score' and the 'frequency plot curve' of the studies, we determined a cutoff value of 6.00. A higher mean score indicated a lower risk of bias, with studies scoring above the cutoff mark categorized as low-bias (73%), while 27% fell into the high-bias category. CONCLUSION: Our findings underscore the immense potential of AI-based machine learning (ML) and deep learning (DL) methods in non-invasively determining MGMT promoter methylation status. Importantly, the clinical significance of these AI-driven advancements lies in their capacity to transform GBM patient care by providing accurate and timely information for treatment decisions. However, the translation of these technical advancements into clinical practice presents challenges, including the need for large multi-institutional cohorts and the integration of diverse data types. Addressing these challenges will be critical in realizing the full potential of AI in improving the reliability and accessibility of MGMT estimation while lowering the risk of bias in clinical decision-making.

Effect of learning parameters on the performance of the U-Net architecture for cell nuclei segmentation from microscopic cell images.

Nuclei segmentation of cells is the preliminary and essential step of pathological image analysis. However, robust and accurate cell nuclei segmentation is challenging due to the enormous variability of staining, cell sizes, morphologies, cell adhesion or overlapping of the nucleus. The automation process to find the cell's nuclei is a giant leap in this direction and has an important step toward bioimage analysis using software tools. This article extensively analyzes deep U-Net architecture and has been applied to the Data Science Bowl dataset to segment the cell nuclei. The dataset undergoes various preprocessing tasks such as resizing, intensity normalization and data augmentation prior to segmentation. The complete dataset then undergoes the rigorous training and validation process to find the optimized hyperparameters and then the optimized model selection. The mean (m) ± standard deviation (SD) of Intersection over Union (IoU) and F1-score (Dice score) have been calculated along with accuracy during the training and validation process, respectively. The optimized U-Net model results in a training IoU of 0.94 ± 0.16 (m ± SD), an F1-score of 0.94 ± 0.17 (m ± SD), a training accuracy of 95.54 and validation accuracy of 95.45. With this model, we applied a completely independent test cohort of the dataset and obtained the mean IOU of 0.93, F1-score of 0.9311, and mean accuracy of 94.12, respectively to measure the segmentation performance.

Fused deep learning paradigm for the prediction of o6-methylguanine-DNA methyltransferase genotype in glioblastoma patients: A neuro-oncological investigation.

BACKGROUND: The O6-methylguanine-DNA methyltransferase (MGMT) is a deoxyribonucleic acid (DNA) repairing enzyme that has been established as an essential clinical brain tumor biomarker for Glioblastoma Multiforme (GBM). Knowing the status of MGMT methylation biomarkers using multi-parametric MRI (mp-MRI) helps neuro-oncologists to analyze GBM and its treatment plan. METHOD: The hand-crafted radiomics feature extraction of GBM's subregions, such as edema(ED), tumor core (TC), and enhancing tumor (ET) in the machine learning (ML) framework, was investigated using support vector machine(SVM), K-Nearest Neighbours (KNN), random forest (RF), LightGBM, and extreme gradient boosting (XGB). For tissue-level analysis of the promotor genes in GBM, we used the deep residual neural network (ResNet-18) with 3D architecture, followed by EfficientNet-based investigation for variants as B0 and B1. Lastly, we analyzed the fused deep learning (FDL) framework that combines ML and DL frameworks. RESULT: Structural mp-MRI consisting of T1, T2, FLAIR, and T1GD having a size of 400 and 185 patients, respectively, for discovery and replication cohorts. Using the CV protocol in the ResNet-3D framework, MGMT methylation status prediction in mp-MRI gave the AUC of 0.753 (p < 0.0001) and 0.72 (p < 0.0001) for the discovery and replication cohort, respectively. We presented that the FDL is ∼7% superior to solo DL and ∼15% to solo ML. CONCLUSION: The proposed study aims to provide solutions for building an efficient predictive model of MGMT for GBM patients using deep radiomics features obtained from mp-MRI with the end-to-end ResNet-18 3D and FDL imaging signatures.

Journey of MOSFET from Planar to Gate All Around: A Review.

With the continuous miniaturization in device dimension to reach the expectation raised by semiconductor users, the shape and size of the MOSFET are changing periodically. The journey started in the year 1960, reached the milestone, and still going on to create history. Due to continuous downscaling, the device dimensions have already reached the critical limit and further miniaturization is a challenge. As a result of which some unwanted effects were raised unknowingly to suppress the device performances while entering into nanoscale. To overcome these kinds of barriers, different device architectures were proposed to keep the journey on. This paper focused on those types of advanced structures in MOSFET, which kept Moore's law alive.

Green synthesis and characterization of silver nanoparticles using Eugenia roxburghii DC. extract and activity against biofilm-producing bacteria.

The green synthesis of silver nanoparticles (AgNPs) and their applications have attracted many researchers as the AgNPs are used effectively in targeting specific tissues and pathogenic microorganisms. The purpose of this study is to synthesize and characterize silver nanoparticles from fully expanded leaves of Eugenia roxburghii DC., as well as to test their effectiveness in inhibiting biofilm production. In this study, at 0.1 mM concentration of silver nitrate (AgNO3), stable AgNPs were synthesized and authenticated by monitoring the color change of the solution from yellow to brown, which was confirmed with spectrophotometric detection of optical density. The crystalline nature of these AgNPs was detected through an X-Ray Diffraction (XRD) pattern. AgNPs were characterized through a high-resolution transmission electron microscope (HR-TEM) to study the morphology and size of the nanoparticles (NPs). A new biological approach was undertaken through the Congo Red Agar (CRA) plate assay by using the synthesized AgNPs against biofilm production. The AgNPs effectively inhibit biofilm formation and the biofilm-producing bacterial colonies. This could be a significant achievement in contending with many dynamic pathogens.

Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine.

Radiogenomics, a combination of "Radiomics" and "Genomics," using Artificial Intelligence (AI) has recently emerged as the state-of-the-art science in precision medicine, especially in oncology care. Radiogenomics syndicates large-scale quantifiable data extracted from radiological medical images enveloped with personalized genomic phenotypes. It fabricates a prediction model through various AI methods to stratify the risk of patients, monitor therapeutic approaches, and assess clinical outcomes. It has recently shown tremendous achievements in prognosis, treatment planning, survival prediction, heterogeneity analysis, reoccurrence, and progression-free survival for human cancer study. Although AI has shown immense performance in oncology care in various clinical aspects, it has several challenges and limitations. The proposed review provides an overview of radiogenomics with the viewpoints on the role of AI in terms of its promises for computational as well as oncological aspects and offers achievements and opportunities in the era of precision medicine. The review also presents various recommendations to diminish these obstacles.

Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework.

Brain tumor characterization (BTC) is the process of knowing the underlying cause of brain tumors and their characteristics through various approaches such as tumor segmentation, classification, detection, and risk analysis. The substantial brain tumor characterization includes the identification of the molecular signature of various useful genomes whose alteration causes the brain tumor. The radiomics approach uses the radiological image for disease characterization by extracting quantitative radiomics features in the artificial intelligence (AI) environment. However, when considering a higher level of disease characteristics such as genetic information and mutation status, the combined study of "radiomics and genomics" has been considered under the umbrella of "radiogenomics". Furthermore, AI in a radiogenomics' environment offers benefits/advantages such as the finalized outcome of personalized treatment and individualized medicine. The proposed study summarizes the brain tumor's characterization in the prospect of an emerging field of research, i.e., radiomics and radiogenomics in an AI environment, with the help of statistical observation and risk-of-bias (RoB) analysis. The PRISMA search approach was used to find 121 relevant studies for the proposed review using IEEE, Google Scholar, PubMed, MDPI, and Scopus. Our findings indicate that both radiomics and radiogenomics have been successfully applied aggressively to several oncology applications with numerous advantages. Furthermore, under the AI paradigm, both the conventional and deep radiomics features have made an impact on the favorable outcomes of the radiogenomics approach of BTC. Furthermore, risk-of-bias (RoB) analysis offers a better understanding of the architectures with stronger benefits of AI by providing the bias involved in them.

De novo transcriptome and tissue specific expression analysis of genes associated with biosynthesis of secondary metabolites in Operculina turpethum (L.).

This study reported the first-ever de novo transcriptome analysis of Operculina turpethum, a high valued endangered medicinal plant, using the Illumina HiSeq 2500 platform. The de novo assembly generated a total of 64,259 unigenes and 20,870 CDS (coding sequence) with a mean length of 449 bp and 571 bp respectively. Further, 20,218 and 16,458 unigenes showed significant similarity with identified proteins of NR (non-redundant) and UniProt database respectively. The homology search carried out against publicly available database found the best match with Ipomoea nil sequences (82.6%). The KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis identified 6538 unigenes functionally assigned to 378 modules with phenylpropanoid biosynthesis pathway as the most enriched among the secondary metabolite biosynthesis pathway followed by terpenoid biosynthesis. A total of 17,444 DEGs were identified among which majority of the DEGs (Differentially Expressed Gene) involved in secondary metabolite biosynthesis were found to be significantly upregulated in stem as compared to root tissues. The qRT-PCR validation of 9 unigenes involved in phenylpropanoid and terpenoid biosynthesis also showed a similar expression pattern. This finding suggests that stem tissues, rather than root tissues, could be used to prevent uprooting of O. turpethum in the wild, paving the way for the plant's effective conservation. Moreover, the study formed a valuable repository of genetic information which will provide a baseline for further molecular research.

Artificial intelligence-based hybrid deep learning models for image classification: The first narrative review.

BACKGROUND: Artificial intelligence (AI) has served humanity in many applications since its inception. Currently, it dominates the imaging field-in particular, image classification. The task of image classification became much easier with machine learning (ML) and subsequently got automated and more accurate by using deep learning (DL). By default, DL consists of a single architecture and is termed solo deep learning (SDL). When two or more DL architectures are fused, the result is termed a hybrid deep learning (HDL) model. The use of HDL models is becoming popular in several applications, but no review of these uses has been designed thus far. Therefore, this study provides the first narrative HDL review by considering all facets of image classification using AI. APPROACH: Our review employs a PRISMA search strategy using Google Scholar, PubMed, IEEE, and Elsevier Science Direct, through which 127 relevant HDL studies were considered. Based on the computer vision evolution, HDLs were subsequently classified into three categories (spatial, temporal, and spatial-temporal). Each study was then analyzed based on several attributes, including continent, publisher, hybridization of two DL or ML, architecture layout, application type, data set type, dataset size, feature extraction methodology, connecting classifier, performance evaluation metrics, and risk-of-bias. CONCLUSION: The HDL models have shown stable and superior performance by taking the best aspects of two or more solo DL or fusion of DL with ML models. Our findings indicate that HDL is being applied aggressively to several medical and non-medical applications. Furthermore, risk-of-bias is highly debatable for DL and HDL models.