Diverse Class Research Articles

LeafNST: an improved data augmentation method for classification of plant disease using object-based neural style transfer

Plant diseases significantly threaten global agriculture, impacting crop yield and food security. Nearly 30% of the crop yield is lost due to plant diseases. Efficient identification and classification of plant diseases through computer vision techniques have become imperative for timely intervention. However, popular plant disease datasets often suffer from data imbalance, with certain classes underrepresented, hindering the performance of machine learning models. Traditional data augmentation methods, such as rotation and flipping, are limited in their effectiveness, especially when faced with imbalanced datasets. To address this limitation, we explore advanced data augmentation techniques, including Generative Adversarial Networks (GANs) such as CycleGAN and LeafGAN, which have shown promise in generating synthetic images. However, we propose an innovative approach of Object-based single Style Transfer on a single neural network for augmenting the plant disease dataset. This technique focuses on mitigating data imbalance issues within datasets, which can adversely affect the model’s ability to generalize across diverse classes. The proposed method is compared with state-of-the-art data augmentation techniques, highlighting its superiority in addressing data imbalance issues. Our approach aims to produce more realistic and diverse synthetic images, leading to improved model generalization and accuracy in plant disease classification tasks validated using different classifiers. The efficiency of our approach is validated through extensive experimentation and benchmarking against existing methods.

Hydrogen solubility in different chemicals: A modelling approach and review of literature data

Hydrogen (H2) solubility is a crucial parameter for industrial processes. This study utilises various Machine Learning (ML) techniques, including Decision Tree (DT), Multilayer Perceptron (MLP), Random Forest (RF), and Extremely Randomized Trees (ET), to predict H2 solubility across diverse chemical classes, encompassing n-alkanes, aliphatic and aromatic hydrocarbons, alcohols, aldehydes, diols, esters, ethers, ketones, and halides. An extensive database of 4337 samples of H2 solubility in 100 different chemicals was compiled from open literature. Problematic samples were identified through preprocessing. Three input sets were utilised for model development to determine the optimal combination of regressors. It was found that including dimensionless pressure and temperature significantly improved modelling accuracy. Higher accuracy was achieved by employing polynomial extracted features. 76 chemicals trained the model, and 20 tested it, with none overlapping. High accuracy on the test set shows applicability to a diverse chemical range beyond the study's dataset. The study's modelling results indicate RF and ET ensemble methods outperform DT and MLP in accuracy and stability, with ET being the most accurate. This study improves our understanding of H2 solubility in various chemicals, crucial for industrial processes like designing H2-based renewable energy plants. Our study advances ML in predicting chemical solubility by introducing advanced feature extraction and testing models with unseen chemicals. This not only validates our models' predictive capability but also yields deeper insights into H2 solubility. With standardized data preprocessing and a model adept at handling diverse chemicals, we demonstrate a substantial advancement in the field through a meticulous testing methodology.

Neurolipid systems: A new target for the treatment of dementia.

AbstractNeurolipids comprise a diverse class of bioactive lipids that include molecules capable of activating G protein‐coupled receptors, thereby inducing systemic effects that contribute to the maintenance of homeostasis. Dementia, a non‐specific brain disorder characterized by a common set of signs and symptoms, usually arises subsequent to brain injuries or diseases and is often associated with the aging process. Individuals affected by dementia suffer from the disruption of several neurotransmitter and neuromodulatory systems, among which neurolipids play an important role, including the endocannabinoid, lysophosphatidic acid and sphingosine 1‐phosphate systems. In this review, we present an overview of the most recent and pertinent findings regarding the involvement of these neurolipidic systems in dementia, including data from a wide range of both in vitro and in vivo experiments as well as clinical trials.

Insights into bioactive constituents of onion (Allium cepa L.) waste: a comparative metabolomics study enhanced by chemometric tools

BackgroundOnion waste was reported to be a valuable source of bioactive constituents with potential health-promoting benefits. This sparked a surge of interest among scientists for its valorization. This study aims to investigate the chemical profiles of peel and root extracts of four onion cultivars (red, copper-yellow, golden yellow and white onions) and evaluate their erectogenic and anti-inflammatory potentials.MethodsUPLC-QqQ-MS/MS analysis and chemometric tools were utilized to determine the chemical profiles of onion peel and root extracts. The erectogenic potential of the extracts was evaluated using the PDE-5 inhibitory assay, while their anti-inflammatory activity was determined by identifying their downregulating effect on the gene expression of IL-6, IL-1β, IFN-γ, and TNF-α in LPS-stimulated WBCs.ResultsA total of 103 metabolites of diverse chemical classes were identified, with the most abundant being flavonoids. The organ’s influence on the chemical profiles of the samples outweighed the influence of the cultivar, as evidenced by the close clustering of samples from the same organ compared to the distinct separation of root and peel samples from the same cultivar. Furthermore, the tested extracts demonstrated promising PDE-5 and anti-inflammatory potentials and effectively suppressed the upregulation of pro-inflammatory markers in LPS-stimulated WBCs. The anti-inflammatory activities exerted by peel samples surpassed those of root samples, highlighting the importance of selecting the appropriate organ to maximize activity. The main metabolites correlated with PDE-5 inhibition were cyanidin 3-O-(malonyl-acetyl)-glucoside and quercetin dimer hexoside, while those correlated with IL-1β inhibition were γ-glutamyl-methionine sulfoxide, γ-glutamyl glutamine, sativanone, and stearic acid. Taxifolin, 3’-hydroxymelanettin, and oleic acid were highly correlated with IL-6 downregulation, while quercetin 4’-O-glucoside, isorhamnetin 4’-O-glucoside, and p-coumaroyl glycolic acid showed the highest correlation to IFN-γ and TNF-α inhibition.ConclusionThis study provides a fresh perspective on onion waste as a valuable source of bioactive constituents that could serve as the cornerstone for developing new, effective anti-PDE-5 and anti-inflammatory drug candidates.

Microbial Exopolysaccharides: Structure, Diversity, Applications, and Future Frontiers in Sustainable Functional Materials

Exopolysaccharides (EPSs) are a diverse class of biopolymers synthesized by microorganisms under environmental stress conditions, such as pH, temperature, light intensity, and salinity. They offer biodegradable and environmentally friendly alternatives to synthetic polymers. Their structural versatility and functional properties make them unique in various industries, including food, pharmaceuticals, biomedicine, cosmetics, textiles, petroleum, and environmental remediation. In this way, among the well-known EPSs, homopolysaccharides like dextran, bacterial cellulose, curdlan, and levan, as well as heteropolysaccharides like xanthan gum, alginate, gellan, and kefiran, have found widespread applications in numerous fields. However, recent attention has focused on the potential role of extremophile bacteria in producing EPSs with novel and unusual protective and biological features under extreme conditions. Therefore, this review provides an overview of the functional properties and applications of the commonly employed EPSs. It emphasizes their importance in various industries and scientific endeavors while highlighting the raised interest in exploring EPSs with novel compositions, structures, and properties, including underexplored protective functionalities. Nevertheless, despite the potential benefits of EPSs, challenges persist. Hence, this review discusses these challenges, explores opportunities, and outlines future directions, focusing on their impact on developing innovative, sustainable, and functional materials.

Tagpyrrollins A and B and Tagpyrrollidone A: Three Pyrrole Steroid Analogues with AKR1B1-Targeting Inhibitory Activity from the Sponges Stylissa massa and Pseudospongosorites suberitoides.

Pyrrole alkaloids (PAs) are a diverse class of natural products with complex carbon frameworks and broad bioactivities that are usually derived from marine sponges. Stylissa massa and Pseudospongosorites suberitoides are two independent sponges collected from the South China Sea in 2013 and 2018, respectively. We discovered PAs are common constituents in both two sponges; more specifically, S. massa produces pyrrole-imidazole alkaloids, and P. suberitoides contains pyrrolidone alkaloids. In this study, three pyrrole steroid metabolites were obtained. Compounds 1 and 2 are a pair of epimers sharing a new 5/7/5/6/6 pentacyclic structural configuration, and compound 3 has a new rigid 5/6/6 tricyclic structure. Interestingly, their scaffolds all possess a 6/6 bicyclic system on the featured classic pyrrole/pyrrolidone skeletons, so-dubbed tagpyrrollins A and B (1 and 2, respectively) and tagpyrrollidone A (3). From a biosynthetic viewpoint, 4,5-dihydroxypent-2-enal probably plays a crucial role in constructing these pyrrole steroid analogues. Based on our previous study on the inhibitory activity of spongiacidin targeting AKR1B1, a drug target for the treatment of chronic diabetic complications, in this study we found that tagpyrrolin A (1) also exhibits an inhibitory effect against AKR1B1.

Copper-Catalyzed Remote Regio- and Enantioselective Yne-Allylic Substitution of Coumarins.

Chiral coumarins and their derivatives are prominent bioactive structural units present in a wide range of natural products and pharmaceutical candidates. Therefore, the development of straightforward and efficient methodologies for the synthesis of readily functionalized chiral coumarins is of significant interest. Herein we report an enantioselective copper-catalyzed yne-allylic substitution of coumarins, resulting in a highly regioselective synthesis of diverse new classes of chiral coumarin derivatives with high efficiency and excellent functional group tolerance. Subsequent versatile transformations further demonstrate the substantial synthetic potential of this strategy in the field of biochemical research.

LVPocket: integrated 3D global-local information to protein binding pockets prediction with transfer learning of protein structure classification

BackgroundPrevious deep learning methods for predicting protein binding pockets mainly employed 3D convolution, yet an abundance of convolution operations may lead the model to excessively prioritize local information, thus overlooking global information. Moreover, it is essential for us to account for the influence of diverse protein folding structural classes. Because proteins classified differently structurally exhibit varying biological functions, whereas those within the same structural class share similar functional attributes.ResultsWe proposed LVPocket, a novel method that synergistically captures both local and global information of protein structure through the integration of Transformer encoders, which help the model achieve better performance in binding pockets prediction. And then we tailored prediction models for data of four distinct structural classes of proteins using the transfer learning. The four fine-tuned models were trained on the baseline LVPocket model which was trained on the sc-PDB dataset. LVPocket exhibits superior performance on three independent datasets compared to current state-of-the-art methods. Additionally, the fine-tuned model outperforms the baseline model in terms of performance.Scientific contributionWe present a novel model structure for predicting protein binding pockets that provides a solution for relying on extensive convolutional computation while neglecting global information about protein structures. Furthermore, we tackle the impact of different protein folding structures on binding pocket prediction tasks through the application of transfer learning methods.Graphical

Phytochemical profiling of Livistona carinensis leaf extract via UHPLC-QTOF-MS/MS with assessment of its antiviral mechanisms.

Among 36 species of the genus Livistona (family Palmae or Arecaceae), L. carinensis is considered the only species native to Africa. Previous studies showed the richness of Livistona fruits in phenolic compounds. The goal of the current study was to investigate the phytochemical composition and assess the antiviral mechanisms of the L. carinensis leaves' ethanolic extract cultivated in Egypt for the first time. The ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS/MS) was applied. Moreover, the total crude extract was fractionated using ethyl acetate and n-butanol for phytochemical investigations by various chromatographic and spectroscopic techniques. Besides, the antiviral activity of L. carinensis leaves was assessed using three protocols in vitro using MTT assay compared to acyclovir. UHPLC-QTOF-MS/MS-based analysis resulted in identification of 72 metabolites tentatively. They belonged to diverse phytochemical classes, mainly including flavonoids (29), organic acids (10), and phenolic acids (7). The antiviral activity investigations revealed a direct Adeno virus inactivation mechanism rather than inhibition of virus replication or blocking its attachment to Vero cells. Hence, the plant leaves may be a potential candidate for discovery of novel antiviral drugs owing to the diversity of identified phytochemical classes.

CVTNet: A Fusion of Convolutional Neural Networks and Vision Transformer for Wetland Mapping Using Sentinel-1 and Sentinel-2 Satellite Data

Wetland mapping is a critical component of environmental monitoring, requiring advanced techniques to accurately represent the complex land cover patterns and subtle class differences innate in these ecosystems. This study aims to address these challenges by proposing CVTNet, a novel deep learning (DL) model that integrates convolutional neural networks (CNNs) and vision transformer (ViT) architectures. CVTNet uses channel attention (CA) and spatial attention (SA) mechanisms to enhance feature extraction from Sentinel-1 (S1) and Sentinel-2 (S2) satellite data. The primary goal of this model is to achieve a balanced trade-off between Precision and Recall, which is essential for accurate wetland mapping. The class-specific analysis demonstrated CVTNet’s proficiency across diverse classes, including pasture, shrubland, urban, bog, fen, and water. Comparative analysis showed that CVTNet outperforms contemporary algorithms such as Random Forest (RF), ViT, multi-layer perceptron mixer (MLP-mixer), and hybrid spectral net (HybridSN) classifiers. Additionally, the attention mechanism (AM) analysis and sensitivity analysis highlighted the crucial role of CA, SA, and ViT in focusing the model’s attention on critical regions, thereby improving the mapping of wetland regions. Despite challenges at class boundaries, particularly between bog and fen, and misclassifications of swamp pixels, CVTNet presents a solution for wetland mapping.

Some Perceptions Differ, Match Outcomes Do Not: A Multisite Retrospective Cross-Sectional Comparison of Virtual vs. In-Person Recruitment.

Virtual interviewing for residency provides considerable savings. Its impact on match outcomes remains unclear. Evaluate the impact of virtual residency recruitment on program and applicant assessment and match outcomes. Cross-sectional survey, September 2020-July 2021 PARTICIPANTS: Faculty interviewers and 2019 and 2020 PGY-1 classes at three academic internal medicine residencies. Survey items rating effectiveness of interview format, preference for future interview format, and perceived impact on diversity. A total of 247/436 faculty (57%) interviewers responded. Faculty perceived that in-person interviews enhanced applicant assessment (3.23 ± 0.38, p < 0.01) and recruitment of the most qualified applicants (p < 0.01) but did not impact recruitment of a racially or gender diverse class (3.03 ± 0.99, p = 0.95 and 3.09 ± 0.76, p = 0.14 respectively). They also did not demonstrate a preference for future interview formats. A total of 259/364 matched applicants responded, corresponding to a 76% response rate in the in-person cohort and a 66% response rate for virtual. Trainees were equally likely to match at their top choice when interviewing virtually vs. in-person (p = 0.56), and racial/ethnic and gender composition of the incoming class also did not differ (p = 0.81 and p = 0.19 respectively). Trainees perceived many aspects of the institution were better assessed in-person, though the impact varied according to assessment domain. Trainees who interviewed in-person preferred in-person formats. Of those who interviewed virtually, 47% preferred virtual and 54% preferred in-person. There were no predictors of virtual preference for future interview formats. Faculty and applicants who experienced virtual recruitment had no preference for future recruitment format. Virtual recruitment had no impact on the racial/gender diversity of matched classes or on applicants matching at their top-ranked institution. Institutions should consider the potential non-inferiority of virtual interviews with financial and other benefits when making decisions about future interview formats.

Rhodopsin-based light-harvesting system for sustainable synthetic biology.

Rhodopsins, a diverse class of light-sensitive proteins found in various life domains, have attracted considerable interest for their potential applications in sustainable synthetic biology. These proteins exhibit remarkable photochemical properties, undergoing conformational changes upon light absorption that drive a variety of biological processes. Exploiting rhodopsin's natural properties could pave the way for creating sustainable and energy-efficient technologies. Rhodopsin-based light-harvesting systems offer innovative solutions to a few key challenges in sustainable engineering, from bioproduction to renewable energy conversion. In this opinion article, we explore the recent advancements and future possibilities of employing rhodopsins for sustainable engineering, underscoring the transformative potential of these biomolecules.

Expedition of Trichoderma formulations: Production to storage in India-A review

The usage of biopesticides is far less compared to chemical pesticides available in the market. Trichoderma is an effective biocontrol agent with a long history of success stories compared to all other available biocontrol agents. Application of Trichoderma formulation enhances the plant growth and is more effective in managing diseases of crop plants. In India, several popular products derived from different Trichoderma species have found commercial success among organic growers. In India, certain quantity of fruitful produces are from diverse class of Trichoderma hence, it must be commercialized. Many small and medium entrepreneurs have recently started producing biocontrol agents for commercial use, which has led to the introduction of numerous biocontrol products in the global market. This paper aims to review and discuss the carrier materials, mass production techniques, target diseases, recommended dose, application methods, storage and shelf life of Trichoderma formulations. The main obstacle inthe commercialization of bioproducts is reduced shelf life. Therefore, this review will explain the various types of Trichoderma formulations in several substrates and commercial viability of the substrates. Key words: Efficacy, Formulation, Substrates, Shelf life, Trichoderma

Natural Guardians: Natural Compounds as Radioprotectors in Cancer Therapy.

Cancer remains a significant global health challenge, with millions of deaths attributed to it annually. Radiotherapy, a cornerstone in cancer treatment, aims to destroy cancer cells while minimizing harm to healthy tissues. However, the harmful effects of irradiation on normal cells present a formidable obstacle. To mitigate these effects, researchers have explored using radioprotectors and mitigators, including natural compounds derived from secondary plant metabolites. This review outlines the diverse classes of natural compounds, elucidating their roles as protectants of healthy cells. Furthermore, the review highlights the potential of these compounds as radioprotective agents capable of enhancing the body's resilience to radiation therapy. By integrating natural radioprotectors into cancer treatment regimens, clinicians may improve therapeutic outcomes while minimizing the adverse effects on healthy tissues. Ongoing research in this area holds promise for developing complementary strategies to optimize radiotherapy efficacy and enhance patient quality of life.

Nanocarriers: Exploring the Potential of Oligonucleotide Delivery.

Nanoparticle-based delivery systems have emerged as promising tools in oligonucleotide therapeutics, facilitating precise and targeted delivery to address several disease conditions. The multifaceted landscape of nanoparticle-based oligonucleotide delivery encompasses the fundamental aspects of nanotechnology in delivery systems, various classes of oligonucleotides, and the growing field of ON-based therapeutics. These ON-based therapeutics are utilized to target specific genetic sequences within cells, offering promising avenues for treating various diseases by regulating gene expression or interfering with specific cellular processes. The integration of nanotechnology in delivery systems offers several advantages, given their intricate systems. Being a diverse class of agents, oligonucleotides provide a wide range of potential owed to each class of agents that support therapeutic interventions. Oligonucleotide-based platforms have demonstrated their versatility in molecular targeting and intervention strategies. Moreover, the complexities and delivery challenges in oligonucleotide therapeutics are expected to be overcome by the application of nanotechnology-based platforms.Because nanoparticles can overcome biological barriers and improve bioavailability, stability, and specificity, their role in developing oligonucleotide delivery systems is greatly valued. The innovative solutions facilitated by nanoparticles are efficient strategies to address the arduous barriers. These strategies beat obstacles like enzymatic degradation, cellular uptake, and immune response, which in turn paves the way for enhanced therapeutic efficacy. This review paper intends to explore the various applications of nanoparticle-mediated oligonucleotide delivery in a variety of diseases. It outlines the promising growth of therapies enabled by these systems, extending from cancer to genetic disorders, neurodegenerative diseases, etc. We have underscored the pivotal role of nanoparticle-based delivery systems in uncovering the full potential of oligonucleotide therapeutics, thereby fostering advancements in precision medicine and targeted therapies.

In Silico Phototoxicity Prediction of Drugs and Chemicals by using Derek Nexus and QSAR Toolbox.

Phototoxicity testing is crucial for evaluating the potential harmful effects of pharmaceuticals and chemicals on human skin when exposed to sunlight. Traditional in vivo models involving mice, rats, guinea pigs, as well as in vitro assays such as the 3T3 Neutral Red Uptake phototoxicity assay and methods based on the use of reconstructed human epidermis, have been established for phototoxicity testing. While these approaches are extremely valuable, they are costly in terms of both time and resources. Consequently, in silico approaches based on the use of predictive software tools can offer more rapid and cost-effective phototoxicity screening solutions. With this goal in mind, the current study evaluated two in silico tools - Derek Nexus 6.1.0/Derek Knowledge Base 2020 1.0 (Lhasa Limited, UK) and the QSAR Toolbox (v 4.5) developed by the Organisation for Economic Co-operation and Development (OECD) - for their capacity to predict the phototoxicity of several substances from diverse classes. Derek Nexus and the QSAR Toolbox were both found to be very useful for predicting the phototoxicity of drugs and other chemicals. Derek Nexus predicted phototoxicity of the compounds, with a sensitivity of 63%, specificity of 93%, Positive Predictive Values of 90% and Negative Predictive Value of 69%, overall accuracy of 77% and balanced accuracy of 78%. The QSAR Toolbox achieved sensitivity of 73%, specificity of 85%, Positive Predictive Value of 85% and Negative Predictive Value of 74%, overall accuracy of 79% and balanced accuracy of 79%. The results show that Derek Nexus and the QSAR Toolbox can be usefully incorporated in the workflow of phototoxicity testing for pharmaceuticals and chemicals.

In-depth metabolite characterization of seaweed-based plant biostimulants: Insights into bioactive components

Biostimulants derived from Kappaphycus alvarezii are renowned for sustainable agricultural enhancement, yet the exploration of numerous other tropical algae remains uncharted. This study aimed to unravel the metabolite diversity, bioactivities, and potential plant biostimulant properties of extracts derived from Kappaphycus alvarezii (KAE), Gracilaria debilis (GDE), and Sargassum cinctum (SCE). Two-year field trials showcased a significant boost in maize grain yields, indicating the biostimulant potential of each extract.Application of 7.5 % seaweed extract concentration substantially increased grain yields by 32 %, 29 %, and 31 % over the control for KAE, GDE, and SCE in the first-year trial and by 31 %, 28 %, and 32 % in the second year. Even a lower concentration of 2.5 % resulted in an increase in grain yield over the control. High-resolution chromatographic techniques coupled with tandem mass spectrometry (MS/MS) revealed 198, 202, and 191 compounds in KAE, GDE, and SCE, respectively. GC–MS profiling further unveiled 23, 21, and 12 metabolites, respectively. The extracts from red and brown seaweeds harbored distinct metabolites with low similarities to each other. These extracts contained a spectrum of compounds, including glycerol, glycine, valine, ellagic acid, cromakalim, genistein, 1,4-β-d-glucan, polyamines, n1-acetylspermine, mannose, propionic acid, benzoic acid, glycine, coumaric acid, hydroxycinnamic acids, and resveratrol. These compounds, known for their potent bio-stimulatory properties in various crops when applied exogenously in pure forms. This study transcends the conventional understanding of seaweed extract efficacy by shedding light on lesser-known metabolites which might have potential biostimulant roles. In contrast to the well-established components traditionally associated with seaweed extracts, this exploration highlights the need to test the biological activities of several other overlooked compounds spanning diverse classes.

Consensus statement on extracellular vesicles in liquid biopsy for advancing laboratory medicine.

Extracellular vesicles (EVs) represent a diverse class of nanoscale membrane vesicles actively released by cells. These EVs can be further subdivided into categories like exosomes and microvesicles, based on their origins, sizes, and physical attributes. Significantly, disease-derived EVs have been detected in virtually all types of body fluids, providing a comprehensive molecular profile of their cellular origins. As a result, EVs are emerging as a valuable addition to liquid biopsy techniques. In this collective statement, the authors share their current perspectives onEV-related research and product development, with a sharedcommitment to translating this newfound knowledge into clinical applications for cancer and other diseases, particularly as disease biomarkers. The consensus within this document revolves around the overarching recognition of the merits, unresolved questions, and existing challenges surrounding EVs. This consensus manuscript is a collaborative effort led by the Committee of Exosomes, Society of Tumor Markers, Chinese anti-Cancer Association, aimed at expediting the cultivation of robust scientific and clinically applicable breakthroughs and propelling the field forward with greater swiftness and efficacy.

TRLS-04. EARLY INSIGHTS FROM PNOC027: PERSONALIZED TREATMENT APPROACHES BASED ON REAL-TIME DRUG SCREENING AND GENOMIC TESTING IN PEDIATRIC AND ADOLESCENT PATIENTS WITH RECURRENT MEDULLOBLASTOMA

Abstract BACKGROUND PNOC027, a clinical trial conducted by the Pediatric Neuro-Oncology Consortium (PNOC), explores a precision medicine strategy with up to four FDA-approved agents integrating real-time drug screening with whole exome DNA and RNA sequencing to guide personalized treatment approaches for children and young adults with relapsed medulloblastoma. METHODS Freshly isolated tumor cells from patients undergo high-throughput drug screening, evaluating responses to 232 clinically available compounds over 72 hours. In vitro drug responses are integrated with tumor genomic and transcriptomic data to predict drug combinations for each patient. Drugs are prioritized based on blood-brain barrier permeability, patient age, prior treatments, and other co-morbidities. Correlative studies include the collection of blood to assess circulating tumor DNA (ctDNA) before surgery and during treatment. Health-related quality of life (HRQOL) assessments will also be collected during therapy. RESULTS To date, nine patients have been enrolled. Eight of these have successfully completed real time drug screening with a median turnaround of 7 days from sample receipt. One patient was excluded due to ineligible (non-medulloblastoma) pathology after enrollment. Seven of these had whole exome sequencing, and six had RNA sequencing. Tumor board recommendations were rendered within 21 business days from the date of surgery. Among the diverse drug classes evaluated, histone deacetylase (HDAC) inhibitors, proteasome inhibitors, anthracyclines, DNA crosslinkers, and kinase inhibitors showed the most robust in vitro responses. Six patients received the recommended regimens, experiencing tolerable (grade 1/2) toxicities. Two patients succumbed to disease prior to therapy initiation, three exhibited disease progression while on therapy, and three remain on therapy. CONCLUSION PNOC027 demonstrates preliminary feasibility of real-time drug screening combined with DNA/RNA sequencing to inform targeted therapy selection in clinically relevant timeframes and with potential for disease response.

The metabolic landscape of the bacteria Rhodococcus erythropolis used in the biodesulfurization of petroleum products: an emphasis on 2-hydroxybiphenyl.

Microorganisms produce diverse classes of metabolites under various physiological conditions. Many bacterial strains have been reported to carry out the process of desulfurization in a cost-effective manner by converting dibenzothiophene (DBT) into 2-hydroxybiphenyl (2-HBP) and then using the 2-HBP as a carbon source for growth and development. Key rate-limiting factors and an increased concentration of 2HBP (400µM) affect the biodesulfurization activity of bacteria through the produced metabolites. Thus, this study was designed to explore the nature of the metabolites produced by Rhodococcus erythropolis in the presence of DBT and 2HBP supplemented with a culture medium. A total of 330 metabolites were detected, and the key metabolites identified were 11Z-eicosaenoyl-EA, 1-carboxyethylisoleucine, 1(3)-glyceryl-PGF2alpha, taurine, 2-hydroxynicotinic acid, 4,4-dimethyl-14alpha-hydroxymethyl-5alpha-cholest-8-en-3beta-ol, and 10-nitrooleic acid. The supplementation of DBT and DBT-2HBP resulted in the differential regulation of these metabolites, either through downregulation or overexpression. Furthermore, at high concentrations of 2-HBP, 1-carboxyethylisoleucine, taurine, 2-hydroxynicotinic acid, and nicotinic acid were upregulated. This work proposes that the identified metabolites may play a role in bacteria-mediated desulphurization and could be beneficial in developing a cost-effective method of desulphurization for refining petroleum.