GAN-based generation of realistic 3D volumetric data: A systematic review and taxonomy.

With the massive proliferation of data-driven algorithms, such as deep learning-based approaches, the availability of high-quality data is of great interest. Volumetric data is very important in medicine, as it ranges from disease diagnoses to therapy monitoring. When the dataset is sufficient, models can be trained to help doctors with these tasks. Unfortunately, there are scenarios where large amounts of data is unavailable. For example, rare diseases and privacy issues can lead to restricted data availability. In non-medical fields, the high cost of obtaining enough high-quality data can also be a concern. A solution to these problems can be the generation of realistic synthetic data using Generative Adversarial Networks (GANs). The existence of these mechanisms is a good asset, especially in healthcare, as the data must be of good quality, realistic, and without privacy issues. Therefore, most of the publications on volumetric GANs are within the medical domain. In this review, we provide a summary of works that generate realistic volumetric synthetic data using GANs. We therefore outline GAN-based methods in these areas with common architectures, loss functions and evaluation metrics, including their advantages and disadvantages. We present a novel taxonomy, evaluations, challenges, and research opportunities to provide a holistic overview of the current state of volumetric GANs.

Mitral Annular Calcification as a Potential False-Positive for Cardiac Amyloidosis in 99m Tc-DPD Scintigraphy Accurately Identified by SPECT/CT.

ABSTRACT: 99m Tc-PYP/DPD/HDMP cardiac scintigraphy has a pivotal role in the diagnosis of ATTR cardiac amyloidosis. The combined findings of a Perugini visual score of 2 or 3 in the scan and the absence of monoclonal proteins in blood and urine are highly specific for the diagnosis of ATTR cardiac amyloidosis without a tissue biopsy. We report a case of mitral annular and valve calcification accurately identified in the SPECT/CT, but which could be misinterpreted as ATTR cardiac amyloidosis if only acquiring planar and SPECT images.

Classification of Pulmonary Nodules in 2-[18F]FDG PET/CT Images with a 3D Convolutional Neural Network.

Purpose: 2-[18F]FDG PET/CT plays an important role in the management of pulmonary nodules. Convolutional neural networks (CNNs) automatically learn features from images and have the potential to improve the discrimination between malignant and benign pulmonary nodules. The purpose of this study was to develop and validate a CNN model for classification of pulmonary nodules from 2-[18F]FDG PET images. Methods: One hundred thirteen participants were retrospectively selected. One nodule per participant. The 2-[18F]FDG PET images were preprocessed and annotated with the reference standard. The deep learning experiment entailed random data splitting in five sets. A test set was held out for evaluation of the final model. Four-fold cross-validation was performed from the remaining sets for training and evaluating a set of candidate models and for selecting the final model. Models of three types of 3D CNNs architectures were trained from random weight initialization (Stacked 3D CNN, VGG-like and Inception-v2-like models) both in original and augmented datasets. Transfer learning, from ImageNet with ResNet-50, was also used. Results: The final model (Stacked 3D CNN model) obtained an area under the ROC curve of 0.8385 (95% CI: 0.6455-1.0000) in the test set. The model had a sensibility of 80.00%, a specificity of 69.23% and an accuracy of 73.91%, in the test set, for an optimised decision threshold that assigns a higher cost to false negatives. Conclusion: A 3D CNN model was effective at distinguishing benign from malignant pulmonary nodules in 2-[18F]FDG PET images. Supplementary Information: The online version contains supplementary material available at 10.1007/s13139-023-00821-6.

Quantitative assessment of dosimetric effect of using alternative OAR delineations in treatment planning as functions of delineations, setup uncertainty and planning techniques using an alternative truth assessment method.

PURPOSE: This study aims to quantify the variation in dose-volume histogram (DVH) and normal tissue complication probability(NTCP) metrics for head-and-neck (HN) cancer patients when alternative organ-at-risk(OAR) delineations are used for treatment planning and for treatment plan evaluation. We particularly focus on the effects of daily patient positioning/setup variations(SV) in relation to treatment technique and delineation variability. MATERIALS AND METHODS: We generated two-arc VMAT, 5-beam IMRT, and 9-beam IMRT treatment plans for a cohort of 209 HN patients. These plans incorporated five different OAR delineation sets, including manual and four automated algorithms. Each treatment plan was assessed under various simulated per-fraction patient setup uncertainties, evaluating the potential clinical impacts through DVH and NTCP metrics. RESULTS: The study demonstrates that increasing SV generally reduces differences in DVH metrics between alternative delineations. However, in contrast, differences in NTCP metrics tend to increase with higher setup variability. This pattern is observed consistently across different treatment plans and delineator combinations, illustrating the intricate relationship between SV and delineation accuracy. Additionally, the need for delineation accuracy in treatment planning is shown to be case-specific and dependent on factors beyond geometric variations. CONCLUSIONS: The findings highlight the necessity for comprehensive Quality Assurance programs in radiotherapy, incorporating both dosimetric impact analysis and geometric variation assessment to ensure optimal delineation quality. The study emphasizes the complex dynamics of treatment planning in radiotherapy, advocating for personalized, case-specific strategies in clinical practice to enhance patient care quality and efficacy in the face of varying SV and delineation accuracies.

Techno-economic Analysis and Optimization of Intensified, Large-Scale Hydrogen Production with Membrane Reactors.

Steam methane reforming (SMR) currently supplies 76% of the world's hydrogen (H2) demand, totaling ∼70 million tonnes per year. Developments in H2 production technologies are required to meet the rising demand for cleaner, less costly H2. Therefore, palladium membrane reactors (Pd-MR) have received significant attention for their ability to increase the efficiency of traditional SMR. This study performs novel economic analyses and constrained, nonlinear optimizations on an intensified SMR process with a Pd-MR. The optimization extends beyond the membrane's operation to present process set points for both the conventional and intensified H2 processes. Despite increased compressor and membrane capital costs along with electric utility costs, the SMR-MR design offers reductions in the natural gas usage and annual costs. Economic comparisons between each plant show Pd membrane costs greater than $25 000/m2 are required to break even with the conventional design for membrane lifetimes of 1-3 years. Based on the optimized SMR-MR process, this study concludes with sensitivity analyses on the design, operational, and cost parameters for the intensified SMR-MR process. Overall, with further developments of Pd membranes for increased stability and lifetime, the proposed SMR-MR design is thus profitable and suitable for intensification of H2 production.

The light spectrum differentially influences morphology, physiology and metabolism of Chrysanthemum × morifolium without affecting biomass accumulation.

The development of light emitting diodes (LED) gives new possibilities to use the light spectrum to manipulate plant morphology and physiology in plant production and research. Here, vegetative Chrysanthemum × morifolium were grown at a photosynthetic photon flux density of 230 µmol m-2 s-1 under monochromatic blue, cyan, green, and red, and polychromatic red:blue or white light with the objective to investigate the effect on plant morphology, gas exchange and metabolic profile. After 33 days of growth, branching and leaf number increased from blue to red light, while area per leaf, leaf weight fraction, flavonol index, and stomatal density and conductance decreased, while dry matter production was mostly unaffected. Plants grown under red light had decreased photosynthesis performance compared with blue or white light-grown plants. The primary and secondary metabolites, such as organic acids, amino acids and phenylpropanoids (measured by non-targeted metabolomics of polar metabolites), were regulated differently under the different light qualities. Specifically, the levels of reduced ascorbic acid and its oxidation products, and the total ascorbate pool, were significantly different between blue light-grown plants and plants grown under white or red:blue light, which imply photosynthesis-driven alterations in oxidative pressure under different light regimens. The overall differences in plant phenotype, inflicted by blue, red:blue or red light, are probably due to a shift in balance between regulatory pathways controlled by blue light receptors and/or phytochrome. Although morphology, physiology, and metabolism differed substantially between plants grown under different qualities of light, these changes had limited effects on biomass accumulation.

Enhancing meat quality of weaned piglets with the dietary incorporation of Ulva lactuca and carbohydrases supplementation.

The impact of the dietary incorporation of 7% Ulva lactuca, a green seaweed, on the quality and nutritional value of piglet's meat was assessed. U. lactuca is rich in nutrients and bioactive compounds but its cell wall is composed of complex polysaccharides that reduce their bioavailability. Therefore, the effect of supplementing piglet diets with exogenous carbohydrases was also assessed here. A total of 40 male weaned piglets were divided into four dietary groups, each with 10 piglets: control (wheat, maize and soybean meal-based diet), UL (7% U. lactuca replacing the control diet), UL + R (UL and 0.005% Rovabio®), and UL + E (UL and 0.01% ulvan lyase). The piglets were fed the diets for 2 weeks. The results showed that incorporating U. lactuca in piglet diets did not influence most of the meat quality traits (P > 0.05). However, the incorporation of U. lactuca with the commercial carbohydrase (UL + R) increased the amount of the docosahexaenoic acid (DHA; 22:6n-3) in their meat (P = 0.011) compared with the control, by 54%. In addition, meat from piglets fed seaweed diets showed a nearly two-fold increase in iodine contents (P < 0.001). Meat tenderness, juiciness and overall acceptability of piglets fed the control diet and the UL diet were lower than those fed the diets containing seaweed and carbohydrases (P < 0.001). Overall, the findings indicate that 7% U. lactuca in the diets of weaned piglets had no major detrimental effects on meat quality and their carbohydrase supplementation has the potential to improve meat sensory traits.

Guanosine treatment prevents lipopolysaccharide-induced depressive-like behavior in mice.

Guanosine is a purinergic nucleoside that has been shown to have neuroprotective effects, mainly through its ability to modulate the glutamatergic system. An increase in pro-inflammatory cytokine levels triggers the activation of the enzyme indoleamine 2,3-dioxygenase 1 (IDO-1), leading to glutamatergic excitotoxicity, which has important roles in the pathophysiology of depression. The aim of this study was to investigate the possible antidepressant-like effects and underlying mechanisms of action of guanosine against lipopolysaccharide (LPS)-induced depression in a mouse model. Mice were orally pre-treated with saline (0.9% NaCl), guanosine (8 or 16 mg/kg), or fluoxetine (30 mg/kg) for 7 days before LPS (0.5 mg/kg, intraperitoneal) injection. One day after LPS injection, mice were subjected to the forced swim test (FST), tail suspension test (TST), and open field test (OFT). After the behavioral tests, mice were euthanized and the levels of tumor necrosis factor-α (TNF-α), IDO-1, glutathione, and malondialdehyde in the hippocampus were measured. Pretreatment with guanosine was able to prevent LPS- induced depressive-like behaviors in the TST and FST. In the OFT, no locomotor changes were observed with any treatment. Both guanosine (8 and 16 mg/kg/day) and fluoxetine treatment prevented the LPS-induced increase in TNF-α and IDO expression and lipid peroxidation as well as decrease of reduced glutathione levels in the hippocampus. Taken together, our findings suggest that guanosine may have neuroprotective effects against LPS-induced depressive-like behavior through preventing oxidative stress and the expression of IDO-1 and TNF-α in the hippocampus.

Towards clinical applicability and computational efficiency in automatic cranial implant design: An overview of the AutoImplant 2021 cranial implant design challenge.

Cranial implants are commonly used for surgical repair of craniectomy-induced skull defects. These implants are usually generated offline and may require days to weeks to be available. An automated implant design process combined with onsite manufacturing facilities can guarantee immediate implant availability and avoid secondary intervention. To address this need, the AutoImplant II challenge was organized in conjunction with MICCAI 2021, catering for the unmet clinical and computational requirements of automatic cranial implant design. The first edition of AutoImplant (AutoImplant I, 2020) demonstrated the general capabilities and effectiveness of data-driven approaches, including deep learning, for a skull shape completion task on synthetic defects. The second AutoImplant challenge (i.e., AutoImplant II, 2021) built upon the first by adding real clinical craniectomy cases as well as additional synthetic imaging data. The AutoImplant II challenge consisted of three tracks. Tracks 1 and 3 used skull images with synthetic defects to evaluate the ability of submitted approaches to generate implants that recreate the original skull shape. Track 3 consisted of the data from the first challenge (i.e., 100 cases for training, and 110 for evaluation), and Track 1 provided 570 training and 100 validation cases aimed at evaluating skull shape completion algorithms at diverse defect patterns. Track 2 also made progress over the first challenge by providing 11 clinically defective skulls and evaluating the submitted implant designs on these clinical cases. The submitted designs were evaluated quantitatively against imaging data from post-craniectomy as well as by an experienced neurosurgeon. Submissions to these challenge tasks made substantial progress in addressing issues such as generalizability, computational efficiency, data augmentation, and implant refinement. This paper serves as a comprehensive summary and comparison of the submissions to the AutoImplant II challenge. Codes and models are available at https://github.com/Jianningli/Autoimplant_II.

Algorithm Recommendation and Performance Prediction Using Meta-Learning.

In the last years, the number of machine learning algorithms and their parameters has increased significantly. On the one hand, this increases the chances of finding better models. On the other hand, it increases the complexity of the task of training a model, as the search space expands significantly. As the size of datasets also grows, traditional approaches based on extensive search start to become prohibitively expensive in terms of computational resources and time, especially in data streaming scenarios. This paper describes an approach based on meta-learning that tackles two main challenges. The first is to predict key performance indicators of machine learning models. The second is to recommend the best algorithm/configuration for training a model for a given machine learning problem. When compared to a state-of-the-art method (AutoML), the proposed approach is up to 130x faster and only 4% worse in terms of average model quality. Hence, it is especially suited for scenarios in which models need to be updated regularly, such as in streaming scenarios with big data, in which some accuracy can be traded for a much shorter model training time.

Augmented Reality-Assisted Ultrasound Breast Biopsy.

Breast cancer is the most prevalent cancer in the world and the fifth-leading cause of cancer-related death. Treatment is effective in the early stages. Thus, a need to screen considerable portions of the population is crucial. When the screening procedure uncovers a suspect lesion, a biopsy is performed to assess its potential for malignancy. This procedure is usually performed using real-time Ultrasound (US) imaging. This work proposes a visualization system for US breast biopsy. It consists of an application running on AR glasses that interact with a computer application. The AR glasses track the position of QR codes mounted on an US probe and a biopsy needle. US images are shown in the user's field of view with enhanced lesion visualization and needle trajectory. To validate the system, latency of the transmission of US images was evaluated. Usability assessment compared our proposed prototype with a traditional approach with different users. It showed that needle alignment was more precise, with 92.67 ± 2.32° in our prototype versus 89.99 ± 37.49° in a traditional system. The users also reached the lesion more accurately. Overall, the proposed solution presents promising results, and the use of AR glasses as a tracking and visualization device exhibited good performance.

Structure-function studies can improve binding affinity of cohesin-dockerin interactions for multi-protein assemblies.

The cellulosome is an elaborate multi-enzyme structure secreted by many anaerobic microorganisms for the efficient degradation of lignocellulosic substrates. It is composed of multiple catalytic and non-catalytic components that are assembled through high-affinity protein-protein interactions between the enzyme-borne dockerin (Doc) modules and the repeated cohesin (Coh) modules present in primary scaffoldins. In some cellulosomes, primary scaffoldins can interact with adaptor and cell-anchoring scaffoldins to create structures of increasing complexity. The cellulosomal system of the ruminal bacterium, Ruminococcus flavefaciens, is one of the most intricate described to date. An unprecedent number of different Doc specificities results in an elaborate architecture, assembled exclusively through single-binding-mode type-III Coh-Doc interactions. However, a set of type-III Docs exhibits certain features associated with the classic dual-binding mode Coh-Doc interaction. Here, the structure of the adaptor scaffoldin-borne ScaH Doc in complex with the Coh from anchoring scaffoldin ScaE is described. This complex, unlike previously described type-III interactions in R. flavefaciens, was found to interact in a dual-binding mode. The key residues determining Coh recognition were also identified. This information was used to perform structure-informed protein engineering to change the electrostatic profile of the binding surface and to improve the affinity between the two modules. The results show that the nature of the residues in the ligand-binding surface plays a major role in Coh recognition and that Coh-Doc affinity can be manipulated through rational design, a key feature for the creation of designer cellulosomes or other affinity-based technologies using tailored Coh-Doc interactions.

Four Weeks of Hericium erinaceus Supplementation Does Not Impact Markers of Metabolic Flexibility or Cognition.

Hericium erinaceus (HE), also known as Lion's Mane mushroom, has been found to enhance cognition and metabolic flexibility in various animal models. To date however, only four studies exist in humans and none have evaluated the effects of HE on markers of metabolic flexibility or cognitive performance. A single-blind, placebo controlled, parallel-longitudinal study was used to determine the effects of HE on markers of metabolic flexibility and cognition. Twenty-four participants completed a graded exercise test on a cycle ergometer to analyze substrate oxidation rates and markers of cardiorespiratory fitness. Additionally, two dual-task challenges consisting of a Stroop Word Challenge interspersed with a Mental Arithmetic Challenge were performed, pre-post the graded exercise test, to evaluate markers of cognition in a pre-post fatigued state. Participants were stratified into two groups, receiving either 10 g of HE per day or placebo for 4-weeks in the form of two muffins identical in taste and appearance. Repeated-measures analysis of variance were conducted to evaluate potential interactions or main effects. Although group differences were noted at baseline, there were no significant interactions or main effects observed from HE ingestion for any dependent variable (all p > 0.05). Our data suggest that ingesting 10 g of HE per day for 4-weeks had no impact on metabolic flexibility and cognition in a college-age cohort. Due to the limited research on HE supplementation, future research is needed to establish an effective supplement dose and duration for potential physiological changes to be observed in humans.

Radiomics in Head and Neck Cancer Outcome Predictions.

Head and neck cancer has great regional anatomical complexity, as it can develop in different structures, exhibiting diverse tumour manifestations and high intratumoural heterogeneity, which is highly related to resistance to treatment, progression, the appearance of metastases, and tumour recurrences. Radiomics has the potential to address these obstacles by extracting quantitative, measurable, and extractable features from the region of interest in medical images. Medical imaging is a common source of information in clinical practice, presenting a potential alternative to biopsy, as it allows the extraction of a large number of features that, although not visible to the naked eye, may be relevant for tumour characterisation. Taking advantage of machine learning techniques, the set of features extracted when associated with biological parameters can be used for diagnosis, prognosis, and predictive accuracy valuable for clinical decision-making. Therefore, the main goal of this contribution was to determine to what extent the features extracted from Computed Tomography (CT) are related to cancer prognosis, namely Locoregional Recurrences (LRs), the development of Distant Metastases (DMs), and Overall Survival (OS). Through the set of tumour characteristics, predictive models were developed using machine learning techniques. The tumour was described by radiomic features, extracted from images, and by the clinical data of the patient. The performance of the models demonstrated that the most successful algorithm was XGBoost, and the inclusion of the patients' clinical data was an asset for cancer prognosis. Under these conditions, models were created that can reliably predict the LR, DM, and OS status, with the area under the ROC curve (AUC) values equal to 0.74, 0.84, and 0.91, respectively. In summary, the promising results obtained show the potential of radiomics, once the considered cancer prognosis can, in fact, be expressed through CT scans.

Ultrasound training simulator using augmented reality glasses: an accuracy and precision assessment study.

Ultrasound (US) imaging despite being safe, cost-effective, and radiation-free, presents poor quality and artifacts, requiring specific medical training in US probe handling and image evaluation. The use of simulators to train physicians has proven its effectiveness, but most of them require specific facilities and hardware. In the last few years, augmented reality has gained relevance to simulate real scenarios which can avoid large setups and broaden medical training to more physicians. This work proposes a new framework for the training of US images acquisition. It consists of a custom-made application that runs on AR glasses (Microsoft HoloLens 2) and interacts with a US simulator application. The AR glasses track the orientation of a QR code mounted on a US probe, communicating its orientation with the US simulator application. This allows the physician to interact with a US probe seeing in real-time the US image in the physician's field of view. The QR code tracking assessment of the AR glasses was conducted by measuring the orientation accuracy and precision when compared with the measures of an electromagnetic tracking device (i.e., NDI Aurora). The proposed solution presented a good performance, rendering the US image in AR glasses with real-time feedback. Moreover, it can track the QR code on the US probe with an accuracy of 0.755°, and a precision of 0.018°. Overall, the proposed framework presents promising results and the use of AR glasses as a tracking device reached a good performance. Clinical Relevance- Simulation is a relevant tool to train physicians, especially in US imaging. AR glasses can broaden the training to less trained physicians by reducing the need for complex setups. This technology allows the implementation of a more natural user interface, which can be relevant in scenarios where good coordination between the eyes and hands of the physician is necessary (i.e., Biopsies).

OpenKBP-Opt: an international and reproducible evaluation of 76 knowledge-based planning pipelines.

Objective.To establish an open framework for developing plan optimization models for knowledge-based planning (KBP).Approach.Our framework includes radiotherapy treatment data (i.e. reference plans) for 100 patients with head-and-neck cancer who were treated with intensity-modulated radiotherapy. That data also includes high-quality dose predictions from 19 KBP models that were developed by different research groups using out-of-sample data during the OpenKBP Grand Challenge. The dose predictions were input to four fluence-based dose mimicking models to form 76 unique KBP pipelines that generated 7600 plans (76 pipelines × 100 patients). The predictions and KBP-generated plans were compared to the reference plans via: the dose score, which is the average mean absolute voxel-by-voxel difference in dose; the deviation in dose-volume histogram (DVH) points; and the frequency of clinical planning criteria satisfaction. We also performed a theoretical investigation to justify our dose mimicking models.Main results.The range in rank order correlation of the dose score between predictions and their KBP pipelines was 0.50-0.62, which indicates that the quality of the predictions was generally positively correlated with the quality of the plans. Additionally, compared to the input predictions, the KBP-generated plans performed significantly better (P< 0.05; one-sided Wilcoxon test) on 18 of 23 DVH points. Similarly, each optimization model generated plans that satisfied a higher percentage of criteria than the reference plans, which satisfied 3.5% more criteria than the set of all dose predictions. Lastly, our theoretical investigation demonstrated that the dose mimicking models generated plans that are also optimal for an inverse planning model.Significance.This was the largest international effort to date for evaluating the combination of KBP prediction and optimization models. We found that the best performing models significantly outperformed the reference dose and dose predictions. In the interest of reproducibility, our data and code is freely available.

Involvement of kynurenine pathway and N-methyl-d-aspartate receptors in the antidepressant-like effect of vilazodone in the tail suspension test in mice.

The present study evaluated the antidepressant-like effects of vilazodone using the tail suspension test in mice. We also investigated the contribution of kynurenine pathway and N-methyl-d-aspartate receptors to this effect. For this purpose, we pretreated animals with sub-effective doses of L-kynurenine, 3-hydroxykynurenine, or quinolinic acid. We then assessed the immobility time, an indicative measure of depressive-like behavior, in the tail suspension test. We also evaluated the possible effects of sub-effective doses of vilazodone combined with sub-effective doses of ketamine (N-methyl-d-aspartate receptor antagonist) in a separate group. Vilazodone (3mg/kg, intraperitoneal) significantly reduced immobility time in the tail suspension test. L-kynurenine (1.7 mg/kg, intraperitoneal), 3-hydroxykynurenine (10 mg/kg, intraperitoneal), and quinolinic acid (3 nmol/site, intracerebroventricular) significantly increased the immobility time in the tail suspension test. The antidepressant-like effects of vilazodone (3mg/kg, intraperitoneal) were inhibited by pre-treatment with non-effective doses of L-kynurenine (0.83 mg/kg, intraperitoneal), 3-hydroxykynurenine (3.33 mg/kg, intraperitoneal), or quinolinic acid (1 nmol/site, intracerebroventricular). Pretreatment of mice with sub-effective doses of ketamine (1 mg/kg, intraperitoneal) optimized the action of a sub-effective dose of vilazodone (0.3mg/kg, intraperitoneal) and reduced the immobility time in the tail suspension test. None of the drugs used in this study induced any changes in locomotor activity in the open field test. The results showed that vilazodone induced an antidepressant-like effect in the tail suspension test, which may be mediated through an interaction with the kynurenine pathway and N-methyl-d-aspartate receptors.

Lipidomics in nutrition research.

PURPOSE OF REVIEW: This review focuses on the recent findings from lipidomics studies as related to nutrition and health research. RECENT FINDINGS: Several lipidomics studies have investigated malnutrition, including both under- and overnutrition. Focus has been both on the early-life nutrition as well as on the impact of overfeeding later in life. Multiple studies have investigated the impact of different macronutrients in lipidome on human health, demonstrating that overfeeding with saturated fat is metabolically more harmful than overfeeding with polyunsaturated fat or carbohydrate-rich food. Diet rich in saturated fat increases the lipotoxic lipids, such as ceramides and saturated fatty-acyl-containing triacylglycerols, increasing also the low-density lipoprotein aggregation rate. In contrast, diet rich in polyunsaturated fatty acids, such as n-3 fatty acids, decreases the triacylglycerol levels, although some individuals are poor responders to n-3 supplementation. SUMMARY: The results highlight the benefits of lipidomics in clinical nutrition research, also providing an opportunity for personalized nutrition. An area of increasing interest is the interplay of diet, gut microbiome, and metabolome, and how they together impact individuals' responses to nutritional challenges.

Metabolic changes in cucumber leaves are enhanced by blue light but differentially affected by UV interactions with light signalling pathways in the visible spectrum.

Ultraviolet radiation (UV, 280-400 nm) as an environmental signal triggers metabolic acclimatory responses. However, how different light qualities affect UV acclimation during growth is poorly understood. Here, cucumber plants (Cucumis sativus) were grown under blue, green, red, or white light in combination with UV. Their effects on leaf metabolites were determined using untargeted metabolomics. Blue and white growth light triggered increased levels of compounds related to primary and secondary metabolism, including amino acids, phenolics, hormones, and compounds related to sugar metabolism and the TCA cycle. In contrast, supplementary UV in a blue or white light background decreased leaf content of amino acids, phenolics, sugars, and TCA-related compounds, without affecting abscisic acid, auxin, zeatin, or jasmonic acid levels. However, in plants grown under green light, UV induced increased levels of phenolics, hormones (auxin, zeatin, dihydrozeatin-7-N-dihydrozeatin, jasmonic acid), amino acids, sugars, and TCA cycle-related compounds. Plants grown under red light with UV mainly showed decreased sugar content. These findings highlight the importance of the blue light component for metabolite accumulation. Also, data on interactions of UV with green light on the one hand, and blue or white light on the other, further contributes to our understanding of light quality regulation of plant metabolism.