Validating alternative oxidase (AOX) gene family as efficient marker consortium for multiple-resilience in Xylella fastidiosa-infected Vitis holobionts.

KEY MESSAGE: AOX gene family in motion marks in-born efficiency of respiration adjustment; can serve for primer screening, genotype ranking, in vitro-plant discrimination and a SMART perspective for multiple-resilient plant holobiont selection. The bacteria Xylella fastidiosa (Xf) is a climate-dependent, global threat to many crops of high socio-economic value, including grapevine. Currently designed breeding strategies for Xf-tolerant or -resistant genotypes insufficiently address the danger of biodiversity loss by focusing on selected threats, neglecting future environmental conditions. Thus, breeding strategies should be validated across diverse populations and acknowledge temperature changes and drought by minimizing the metabolic-physiologic effects of multiple stress-induced oxygen shortages. This research hypothesizes that multiple-resilient plant holobionts achieve lifelong adaptive robustness through early molecular and metabolic responses in primary stress target cells, which facilitate efficient respiration adjustment and cell cycle down-regulation. To validate this concept open-access transcriptome data were analyzed of xylem tissues of Xf-tolerant and -resistant Vitis holobionts from diverse trials and genetic origins from early hours to longer periods after Xf-inoculation. The results indicated repetitive involvement of alternative oxidase (AOX) transcription in episodes of down-regulated transcripts of cytochrome c oxidase (COX) at various critical time points before disease symptoms emerged. The relation between transcript levels of COX and AOX ('relCOX/AOX') was found promising for plant discrimination and primer screening. Furthermore, transcript levels of xylem-harbored bacterial consortia indicated common regulation with Xf and revealed stress-induced early down-regulation and later enhancement. LPS priming promoted the earlier increase in bacterial transcripts after Xf-inoculation. This proof-of-principle study highlights a SMART perspective for AOX-assisted plant selection towards multiple-resilience that includes Xf-tolerance. It aims to support timely future plant diagnostics and in-field substitution, sustainable agro-management, which protects population diversity and strengthens both conventional breeding and high-tech, molecular breeding research. Furthermore, the results suggested early up-regulation of bacterial microbiota consortia in vascular-enriched tissues as a novel additional trait for future studies on Xf-tolerance.

The mutant mouse resource and research center (MMRRC) consortium: the US-based public mouse repository system.

Now in its 25th year, the Mutant Mouse Resource and Research Center (MMRRC) consortium continues to serve the United States and international biomedical scientific community as a public repository and distribution archive of laboratory mouse models of human disease for research. Supported by the National Institutes of Health (NIH), the MMRRC consists of 4 regionally distributed and dedicated vivaria, offices, and specialized laboratory facilities and an Informatics Coordination and Service Center (ICSC). The overarching purpose of the MMRRC is to facilitate groundbreaking biomedical research by offering an extensive repertoire of mutant mice that are essential for advancing the understanding of human physiology and disease. The function of the MMRRC is to identify, acquire, evaluate, characterize, cryopreserve, and distribute mutant mouse strains to qualified biomedical investigators around the nation and the globe. Mouse strains accepted from the research community are held to the highest scientific standards to optimize reproducibility and enhance scientific rigor and transparency. All submitted strains are thoroughly reviewed, documented, and validated using extensive scientific quality control measures. In addition, the MMRRC conducts resource-related research on cryopreservation, mouse genetics, environmental conditions, and other topics that enhance operations of the MMRRC. Today, the MMRRC maintains an archive of mice, cryopreserved embryos and sperm, embryonic stem (ES) cell lines, and murine hybridomas for nearly 65,000 alleles. Since its inception, the MMRRC has fulfilled more than 20,000 orders from 13,651 scientists at 8441 institutions worldwide. The MMRRC also provides numerous services to assist researchers, including scientific consultation, technical assistance, genetic assays, microbiome analysis, analytical phenotyping, pathology, cryorecovery, husbandry, breeding and colony management, infectious disease surveillance, and disease modeling. The ICSC coordinates MMRRC operations, interacts with researchers, and manages the website (mmrrc.org) and online catalogue. Researchers benefit from an expansive list of well-defined mouse models of disease that meet the highest scientific standards while submitting investigators benefit by having their mouse strains cryopreserved, protected, and distributed in compliance with NIH policies.

Lockbox enrichment facilitates manipulative and cognitive activities for mice.

Background: Due to the lack of complexity and variety of stimuli, conventional housing conditions of laboratory mice do not allow these animals to fully express their behavioral repertoire, including manipulative and cognitive activities. Therefore, we designed mechanical puzzles, so-called lockboxes, for mice that can be provided in their home cages. We investigated the impact of the lockbox enrichment on their phenotype and affective state when compared to conventional housing (CH) and super-environmental enrichment (SEE). Methods: Young adult female C57BL/6JCrl mice were examined before and after 2-month exposure to the different types of enrichment in a phenotyping test battery, including tests for trait and state anxiety-related behavior, calorimetric measurements, body weight measurements, the analysis of stress hormone metabolite concentrations, and sequential problem-solving abilities with a novel lockbox. At the end of the study, adrenal gland weights were determined and pathohistological evaluation was performed. For all continuous variables, the relative variability was calculated. Results: While the different types of enrichment affected trait anxiety-related behavior, neither state anxiety-related behavior nor physiological variables (i.e., bodyweight, resting metabolic rate, stress hormone metabolite concentrations, adrenal gland weights) were influenced. LE improved sequential problem-solving (i.e., solving novel lockboxes) when compared to SEE. Regardless of the housing condition, the relative variability increased in most variables over time, although the coefficient of variation decreased for some variables, especially in animals with access to LE. There was no evidence of toxicopathological effects associated with the material from which the lockboxes were made. Conclusions: All lockboxes are available as open-source tool. LE revealed beneficial effects on the affective state of laboratory mice and their performance in solving novel lockboxes. Neither relevant phenotype of the mice nor reproducibility of the data were compromised by LE, similar to SEE. The lockboxes may also be used as novel approach for assessing cognition in mice.

A prioritization tool for cilia-associated genes and their in vivo resources unveil new avenues for ciliopathy research.

Defects in ciliary signaling or mutations in proteins that localize to primary cilia lead to a class of human diseases known as ciliopathies. About 10% of mammalian genes encode cilia-associated proteins and a major gap in the cilia research field is prioritizing which genes to study and finding the in vivo vertebrate mutant alleles and reagents available for their study. Here we present a unified resource listing the cilia-associated human genes cross-referenced to available mouse and zebrafish mutant alleles, their associated phenotypes as well as expression data in kidney and functional data for vertebrate Hedgehog signaling. This resource empowers researchers to easily sort and filter genes based on their own expertise and priorities, cross-reference with newly-generated -omics datasets, and quickly find in vivo resources and phenotypes associated with a gene of interest.

Idiopathic Distal Sensory Polyneuropathy and Fibromyalgia Syndrome: A Comparative Phenotyping Study.

INTRODUCTION: Painful idiopathic distal sensory polyneuropathy (IDSP) and fibromyalgia syndrome (FMS) are cryptogenic chronic pain syndromes. The contribution of small fibre pathology (SFP) in FMS remains controversial. This study aims to quantify small nerve pathology in participants with IDSP and FMS and identify relationships of SFP with sensory phenotypes. METHODS: In this study, 73 individuals (FMS: 25, IDSP: 23, healthy volunteers: 25) underwent comprehensive assessment, including neurological exams, questionnaires, sensory tests, and corneal confocal microscopy. RESULTS: IDSP participants displayed lower wind-up ratio (WUR) relative to FMS (p < 0.001), loss of function to thermal and mechanical stimuli and elevated neuropathy disability scores compared to FMS and healthy volunteers (all p < 0.001). FMS participants demonstrated gain of function to heat and blunt pressure pain responses relative to IDSP, and healthy volunteers (heat: p = 0.002 and p = 0.003; pressure: both p < 0.001) and WUR (both p < 0.001). FMS participants exhibited reduced corneal nerve fibre density (p = 0.02), while IDSP participants had lower global corneal nerve measures (density, branch density, and length) relative to healthy volunteers (all p < 0.001). Utilising corneal nerve fibre length, SFP was demonstrated in 66.6% of participants (FMS: 13/25; IDSP: 22/23). CONCLUSION: Participants with SFP, in both FMS and IDSP, reported symptoms indicative of small nerve fibre disease. Although distinctions in pain distributions are evident between individuals with FMS and IDSP, over 50% of participants between the two conditions displayed both a loss and gain of thermal and mechanical function suggestive of shared mechanisms. However, sensory phenotypes were associated with the presence of SFP in IDSP but not in FMS.

In people with painful idiopathic neuropathy (pain related to nerve damage where the cause of nerve damage is unknown), fibromyalgia syndrome (a long-term condition causing widespread pain), and healthy volunteers, the small nerve fibres of the peripheral nervous system, which may be involved in generating pain were assessed. These nerve fibres can be measured at the front of the eye (cornea) which can provide details on whether they are damaged in the body. The response to temperature, light touch, pressure and pinprick stimuli can also be used to determine if there is a loss or gain of sensation, which may contribute to pain. The aim of this study was to identify the degree of damage to these nerve fibres and to determine whether this damage is associated with a loss (cannot feel or requires more intense stimulus to feel) or gain (stimulus is felt earlier or is painful earlier at lower intensity) of sensory function. The pattern of loss or gain in sensory function is known as a sensory phenotype. It was found that people with painful idiopathic neuropathy had more severe nerve damage, loss of function to temperature and touch, and fewer small nerve fibres in the cornea compared to those with fibromyalgia syndrome and healthy volunteers. People with fibromyalgia syndrome were more sensitive to heat and pressure and had fewer corneal nerve fibres relative to healthy volunteers. The presence of corneal nerve fibre damage was associated with sensory phenotypes (types of sensation felt) in painful idiopathic neuropathy but not in fibromyalgia syndrome.

Multi-omics profiling reveals peripheral blood biomarkers of multiple sclerosis: implications for diagnosis and stratification.

Background: Multiple sclerosis (MS), a chronic autoimmune disorder marked by demyelination in the central nervous system, is exceptionally uncommon in China, and remains poorly understood in terms of its peripheral blood manifestations. Methods: We conducted a cohort study comprising 39 MS patients and 40 normal controls (NC). High-dimensional mass cytometry, protein arrays, and targeted metabolomics were utilized to profile immune subsets, proteins, and metabolites in blood. Differences in multi-omics signatures were scrutinized across varying MS subtypes. Results: Immune profiling demonstrated an elevation in various B cell subsets and monocytes, alongside a reduction in dendritic cells among MS patients. Proteomic data revealed a downregulation in neurotrophic and tissue repair proteins. Metabolomic assessment showed a noted decrease in anti-inflammatory molecules and sphingolipids. Integrated analysis identified distinct molecular patterns distinguishing MS from controls. Additionally, multi-omics differences among different MS subtypes were uncovered. Notably, hippuric acid levels was consistently lower in MS subgroups with greater disease severity. Conclusion: This study represents the pioneering exploration of multi-omics in Chinese MS patients, presenting a comprehensive view of the peripheral blood changes in MS. Our study underscores the robust capability of multi-omics assessments in identifying peripheral blood biomarkers that delineate the varied clinical presentation, and facilitates future development of biomarkers and targeted therapeutic interventions in MS.

Annotated image dataset of fire blight symptoms for object detection in orchards.

The monitoring of plant diseases in nurseries, breeding farms and orchards is essential for maintaining plant health. Fire blight (Erwinia amylovora) is still one of the most dangerous diseases in fruit production, as it can spread epidemically and cause enormous economic damage. All measures are therefore aimed at preventing the spread of the pathogen in the orchard and containing an infection at an early stage [1-6]. Efficiency in plant disease control benefits from the development of a digital monitoring system if the spatial and temporal resolution of disease monitoring in orchards can be increased [7]. In this context, a digital disease monitoring system for fire blight based on RGB images was developed for orchards. Between 2021 and 2024, data was collected on nine dates under different weather conditions and with different cameras. The data source locations in Germany were the experimental orchard of the Julius Kühn Institute (JKI), Institute of Plant Protection in Fruit Crops and Viticulture in Dossenheim, the experimental greenhouse of the Julius Kühn Institute for Resistance Research and Stress Tolerance in Quedlinburg and the experimental orchard of the JKI for Breeding Research on Fruit Crops located in Dresden-Pillnitz. The RGB images were taken on different apple genotypes after artificial inoculation with Erwinia amylovora, including cultivars, wild species and progeny from breeding. The presented ERWIAM dataset contains manually labelled RGB images with a size of 1280  × 1280 pixels of fire blight infected shoots, flowers and leaves in different stages of development as well as background images without symptoms. In addition, symptoms of other plant diseases were acquired and integrated into the ERWIAM dataset as a separate class. Each fire blight symptom was annotated with the Computer Vision Annotation Tool (CVAT [8]) using 2-point annotations (bounding boxes) and presented in YOLO 1.1 format (.txt files). The dataset contains a total of 1611 annotated images and 87 background images. This dataset can be used as a resource for researchers and developers working on digital systems for plant disease monitoring.

Phenotypic characterisation of people at risk of atrial fibrillation: Protocol for the FIND-AF longitudinal cohort study.

AIMS: The Future Innovations in Novel Detection of Atrial Fibrillation (FIND-AF) longitudinal cohort study is a multi-centre prospective cohort study of patients identified at risk of atrial fibrillation (AF). The aim of the FIND-AF longitudinal cohort study is to provide multi-modal phenotypic characterisation of these patients. METHODS AND RESULTS: 1955 participants identified as at risk of AF by the FIND-AF algorithm from primary care electronic health (EHR) data, aged 30 years and above and eligible for oral anticoagulation, will be be recruited between October 2023 and November 2024 to receive home-based intermittent ECG monitoring. About 500 participants without diagnosed AF will then undergo cross-sectional phenotypic characterisation including physical examination, symptoms assessment, serum blood biomarkers and echocardiography, and non-stress cardiac magnetic resonance imaging. Longitudinal information about cardio-renal-metabolic-pulmonary outcomes will be ascertained from linkages to EHR data. The study is funded by the British Heart Foundation (CC/22/250026). The study has ethical approval (North West - Greater Manchester South Research Ethics Committee reference 23/NW/0180). Findings will be announced at relevant conferences and published in peer-reviewed journals in line with the Funder's open access policy. CONCLUSIONS: The FIND-AF multi-centre prospective longitudinal cohort study aims to (i) provide evidence for the impact of comorbidities on AF genesis (ii) uncover actionable targets to prevent AF, and (iii) act as a platform for cohort randomised clinical trials that investigate enhanced detection and prevention of AF.

Atrial fibrillation (AF) is the most common abnormal heart rhythm encountered in clinical practice but we know little about changes to the heart before AF starts, and whether these can be reversed to reduce the risk of future AF. In this study people we will recruit people who have been identified as higher risk of AF using a decision support tool in their medical records, but who have not been found to have AF at the moment when they have had their ECG checked.We will look at the structure and function of their hearts using ultrasound and MRI, and we will also check their blood tests. We aim to learn if people without AF, but at higher risk of AF, have changes to their heart and then conduct studies to establish if these changes can be reversed.

Lifestyle Profiling Using Wearables and Prediction of Glucose Metabolism in Individuals with Normoglycemia or Prediabetes.

This study examined the relationship between lifestyles (diet, sleep, and physical activity) and glucose responses at a personal level. 36 healthy adults in the Bay Area were monitored for their lifestyles and glucose levels using wearables and continuous glucose monitoring (NCT03919877). Gold-standard metabolic tests were conducted to phenotype metabolic characteristics. Through the lifestyle data (2,307 meals, 1,809 nights, and 2,447 days) and 231,206 CGM readings from metabolically-phenotyped individuals with normoglycemia or prediabetes, we found: 1) eating timing was associated with hyperglycemia, muscle insulin resistance (IR), and incretin dysfunction, whereas nutrient intakes were not; 2) timing of increased activity in muscle IS and IR participants was associated with differential benefits of glucose control; 3) Integrated ML models using lifestyle factors predicted distinct metabolic characteristics (muscle, adipose IR or incretin dysfunction). Our data indicate the differential impact of lifestyles on glucose regulation among individuals with different metabolic phenotypes, highlighting the value of personalized lifestyle modifications.

What makes Candida auris pan-drug resistant? Integrative insights from genomic, transcriptomic, and phenomic analysis of clinical strains resistant to all four major classes of antifungal drugs.

The global epidemic of drug-resistant Candida auris continues unabated. The initial report on pan-drug resistant (PDR) C. auris strains in a hospitalized patient in New York was unprecedented. PDR C. auris showed both known and unique mutations in the prominent gene targets of azoles, amphotericin B, echinocandins, and flucytosine. However, the factors that allow C. auris to acquire pan-drug resistance are not known. Therefore, we conducted a genomic, transcriptomic, and phenomic analysis to better understand PDR C. auris. Among 1,570 genetic variants in drug-resistant C. auris, 299 were unique to PDR strains. The whole-genome sequencing results suggested perturbations in genes associated with nucleotide biosynthesis, mRNA processing, and nuclear export of mRNA. Whole transcriptome sequencing of PDR C. auris revealed two genes to be significantly differentially expressed-a DNA repair protein and DNA replication-dependent chromatin assembly factor 1. Of 59 novel transcripts, 12 transcripts had no known homology. We observed no fitness defects among multi-drug resistant (MDR) and PDR C. auris strains grown in nutrient-deficient or -enriched media at different temperatures. Phenotypic profiling revealed wider adaptability to nitrogenous nutrients and increased utilization of substrates critical in upper glycolysis and tricarboxylic acid cycle. Structural modeling of a 33-amino acid deletion in the gene for uracil phosphoribosyl transferase suggested an alternate route in C. auris to generate uracil monophosphate that does not accommodate 5-fluorouracil as a substrate. Overall, we find evidence of metabolic adaptations in MDR and PDR C. auris in response to antifungal drug lethality without deleterious fitness costs.

Identification of host endotypes using peripheral blood transcriptomics in a prospective cohort of patients with endocarditis.

OBJECTIVES: Host responses to infection are a major determinant of outcome. However, the existence of different response profiles in patients with endocarditis has not been addressed. Our objective was to apply transcriptomics to identify endotypes in patients with infective endocarditis. METHODS: A total of 32 patients with infective endocarditis were studied. Clinical data and blood samples were collected at diagnosis and RNA sequenced. Gene expression was used to identify two clusters (endocarditis endotype 1 [EE1] and endocarditis endotype 2 [EE2]). RNA sequencing was repeated after surgery. Transcriptionally active cell populations were identified by deconvolution. Differences between endotypes in clinical data, survival, gene expression, and molecular pathways involved were assessed. The identified endotypes were recapitulated in a cohort of COVID-19 patients. RESULTS: A total of 18 and 14 patients were assigned to EE1 and EE2, respectively, with no differences in clinical data. Patients assigned to EE2 showed an enrichment in genes related to T-cell maturation and a decrease in the activation of the signal transducer and activator of transcription protein family pathway, with higher counts of active T cells and lower counts of neutrophils. A total of 14 patients (nine in EE1 and five in EE2) were submitted to surgery. Surgery in EE2 patients shifted gene expression toward a EE1-like profile. In-hospital mortality was higher in EE1 (56% vs 14%, P = 0.027), with an adjusted hazard ratio of 12.987 (95% confidence interval 3.356-50). Translation of these endotypes to COVID-19 and non-COVID-19 septic patients yielded similar results in cell populations and outcome. CONCLUSIONS: Gene expression reveals two endotypes in patients with acute endocarditis, with different underlying pathogenetic mechanisms, responses to surgery, and outcomes.

A Case Demonstration of the Open Health Natural Language Processing Toolkit From the National COVID-19 Cohort Collaborative and the Researching COVID to Enhance Recovery Programs for a Natural Language Processing System for COVID-19 or Postacute Sequelae of SARS CoV-2 Infection: Algorithm Development and Validation.

BACKGROUND: A wealth of clinically relevant information is only obtainable within unstructured clinical narratives, leading to great interest in clinical natural language processing (NLP). While a multitude of approaches to NLP exist, current algorithm development approaches have limitations that can slow the development process. These limitations are exacerbated when the task is emergent, as is the case currently for NLP extraction of signs and symptoms of COVID-19 and postacute sequelae of SARS-CoV-2 infection (PASC). OBJECTIVE: This study aims to highlight the current limitations of existing NLP algorithm development approaches that are exacerbated by NLP tasks surrounding emergent clinical concepts and to illustrate our approach to addressing these issues through the use case of developing an NLP system for the signs and symptoms of COVID-19 and PASC. METHODS: We used 2 preexisting studies on PASC as a baseline to determine a set of concepts that should be extracted by NLP. This concept list was then used in conjunction with the Unified Medical Language System to autonomously generate an expanded lexicon to weakly annotate a training set, which was then reviewed by a human expert to generate a fine-tuned NLP algorithm. The annotations from a fully human-annotated test set were then compared with NLP results from the fine-tuned algorithm. The NLP algorithm was then deployed to 10 additional sites that were also running our NLP infrastructure. Of these 10 sites, 5 were used to conduct a federated evaluation of the NLP algorithm. RESULTS: An NLP algorithm consisting of 12,234 unique normalized text strings corresponding to 2366 unique concepts was developed to extract COVID-19 or PASC signs and symptoms. An unweighted mean dictionary coverage of 77.8% was found for the 5 sites. CONCLUSIONS: The evolutionary and time-critical nature of the PASC NLP task significantly complicates existing approaches to NLP algorithm development. In this work, we present a hybrid approach using the Open Health Natural Language Processing Toolkit aimed at addressing these needs with a dictionary-based weak labeling step that minimizes the need for additional expert annotation while still preserving the fine-tuning capabilities of expert involvement.

"Anything that would help is a positive development": feasibility, tolerability, and user experience of smartphone-based digital phenotyping for people with and without type 2 diabetes.

Background: Digital phenotyping, the in-situ collection of passive (phone sensor) and active (daily surveys) data using a digital device, may provide new insights into the complex relationship between daily behaviour and mood for people with type 2 diabetes. However, there are critical knowledge gaps regarding its use in people with type 2 diabetes. This study assessed feasibility, tolerability, and user experience of digital phenotyping in people with and without type 2 diabetes after participation in a 2-month digital phenotyping study in Ireland. At study completion, participants rated methodology elements from "not a problem" to a "serious problem" on a 5-point scale and reported their comfort with the potential future use of digital phenotyping in healthcare, with space for qualitative expansion. Results: Eighty-two participants completed baseline. Attrition was 18.8%. Missing data ranged from 9-44% depending on data stream. Sixty-eight participants (82.9%) completed the user experience questionnaire (51.5% with type 2 diabetes; 61.8% female; median age-group 50-59). Tolerability of digital phenotyping was high, with "not a problem" being selected 76.5%-89.7% of the time across questions. People with type 2 diabetes (93.9%) were significantly more likely to be comfortable with their future healthcare provider having access to their digital phenotyping data than those without (53.1%), χ2 (1) = 14.01, p = < .001. Free text responses reflected a range of positive and negative experiences with the study methodology. Conclusions: An uncompensated, 2-month digital phenotyping study was feasible among people with and without diabetes, with low attrition and reasonable missing data rates. Participants found digital phenotyping to be acceptable, and even enjoyable. The potential benefits of digital phenotyping for healthcare may be more apparent to people with type 2 diabetes than the general population. Supplementary Information: The online version contains supplementary material available at 10.1186/s44247-024-00116-6.

Phenotyping lipid profiles in type 2 diabetes: Risk association and outcomes from the Cardiovascular Health Study.

Aims: To determine whether discrete lipid profiles (refer to as lipid phenotyping) can be used to stratify cardiovascular risk in individuals with type 2 diabetes. Methods and results: Cardiovascular Health Study participants with diabetes and fasting lipid profiles at baseline (n = 866) were categorized separately by level of LDL cholesterol and HDL-C/Triglyceride (Tg) profiles (low Tg/high HDL-C; high Tg/low HDL-C; high Tg only or low HDL-C only). We performed Cox multivariate regression analysis to assess the risk of CVD mortality, incident myocardial infarction (MI), heart failure (HF), stroke, and composite MACE (MI, HF, stroke, and CVD mortality) associated with each lipid category. We also calculated risk estimates for MACE using lipid measures as continuous variables. In the fully adjusted model, the high triglyceride plus low HDL-C cholesterol phenotype demonstrated risk that was at least as high as the highest LDL-C sub-group phenotype for CVD mortality (Hazard ratio {HR} 1.58 vs 1.48), MI (HR 1.53 vs 1.58), HF (HR 1.47 vs 1.20), stroke (HR 2.02 vs 1.43), and MACE (HR 1.58 vs 1.38). When modeled continuously, the HR per SD for MACE was 1.12 (p = 0.03) for LDL-C and 1.19-1.20 (p < 0.001) for triglycerides or remnant cholesterol. Conclusions: Participants with the high triglyceride/low HDL-C phenotype had equivalent or higher CVD risk than those with the high LDL-C phenotype. Further studies are necessary to determine whether lipid phenotyping accounts for the substantial CVD risk not explained by LDL cholesterol among individuals with type 2 diabetes.

Clinical Perspectives, Eligibility, and Success Criteria for Bariatric/Metabolic Surgery.

Obesity is a worldwide chronic, complex, and progressive disease that poses a challenge for physicians to pursue optimal therapeutic decision making. This chapter focuses on the definition of obesity, based on excessive fat accumulation, and thus underscores the importance of body composition, and the clinical tools used to diagnose it in the context of excess weight, metabolic alteration, and obesity-associated comorbidity development. Additionally, it addresses the indications for surgery that are currently applicable and the description of the different types of patients who could benefit the most from the surgical management of excessive body fat and its associated metabolic derangements and quality of life improvement. Furthermore, it also highlights plausible underlying mechanisms of action for the beneficial effects following bariatric/metabolic surgery.

UGT2B10 is the Major UDP-Glucuronosyltransferase 2B Isoform Involved in the Metabolism of Lamotrigine and is Implicated in the Drug-Drug Interaction with Valproic Acid.

Lamotrigine is a phenyltriazine anticonvulsant that is primarily metabolized by phase II UDP-glucuronosyltransferases (UGT) to a quaternary N2-glucuronide, which accounts for ~ 90% of the excreted dose in humans. While there is consensus that UGT1A4 plays a predominant role in the formation of the N2-glucuronide, there is compelling evidence in the literature to suggest that the metabolism of lamotrigine is catalyzed by another UGT isoform. However, the exact identity of the UGT isoform that contribute to the formation of this glucuronide remains uncertain. In this study, we harnessed a robust reaction phenotyping strategy to delineate the identities and its associated fraction metabolized (fm) of the UGTs involved in lamotrigine N2-glucuronidation. Foremost, human recombinant UGT mapping experiments revealed that the N2-glucuronide is catalyzed by multiple UGT isoforms. (i.e., UGT1A1, 1A3, 1A4, 1A9, 2B4, 2B7, and 2B10). Thereafter, scaling the apparent intrinsic clearances obtained from the enzyme kinetic experiments with our in-house liver-derived relative expression factors (REF) and relative activity factors (RAF) revealed that, in addition to UGT1A4, UGT2B10 was involved in the N2-glucuronidation of lamotrigine. This was further confirmed via chemical inhibition in human liver microsomes with the UGT1A4-selective inhibitor hecogenin and the UGT2B10-selective inhibitor desloratadine. By integrating various orthogonal approaches (i.e., REF- and RAF-scaling, and chemical inhibition), we quantitatively determined that the fm for UGT1A4 and UGT2B10 ranged from 0.42 - 0.64 and 0.32 - 0.57, respectively. Finally, we also provided nascent evidence that the pharmacokinetic interaction between lamotrigine and valproic acid likely arose from the in vivo inhibition of its UGT2B10-mediated pathway.

Root phenotypes for improved nitrogen capture.

Background: Suboptimal nitrogen availability is a primary constraint for crop production in low-input agroecosystems, while nitrogen fertilization is a primary contributor to the energy, economic, and environmental costs of crop production in high-input agroecosystems. In this article we consider avenues to develop crops with improved nitrogen capture and reduced requirement for nitrogen fertilizer. Scope: Intraspecific variation for an array of root phenotypes has been associated with improved nitrogen capture in cereal crops, including architectural phenotypes that colocalize root foraging with nitrogen availability in the soil; anatomical phenotypes that reduce the metabolic costs of soil exploration, improve penetration of hard soil, and exploit the rhizosphere; subcellular phenotypes that reduce the nitrogen requirement of plant tissue; molecular phenotypes exhibiting optimized nitrate uptake kinetics; and rhizosphere phenotypes that optimize associations with the rhizosphere microbiome. For each of these topics we provide examples of root phenotypes which merit attention as potential selection targets for crop improvement. Several cross-cutting issues are addressed including the importance of soil hydrology and impedance, phenotypic plasticity, integrated phenotypes, in silico modeling, and breeding strategies using high throughput phenotyping for co-optimization of multiple phenes. Conclusions: Substantial phenotypic variation exists in crop germplasm for an array of root phenotypes that improve nitrogen capture. Although this topic merits greater research attention than it currently receives, we have adequate understanding and tools to develop crops with improved nitrogen capture. Root phenotypes are underutilized yet attractive breeding targets for the development of the nitrogen efficient crops urgently needed in global agriculture.

Bridging the mental health gap: The scope, challenges, and future of digital psychiatry in India.

The mental health burden in India is increasing at unprecedented rates. The increased demand for mental health care and the undersupply of services has widened the treatment gap. Due to several factors, such as increasing service costs and the circumstances surrounding the COVID-19 pandemic, India has witnessed an inclination toward using digital mental health solutions to overcome the treatment gap. Drawing from the collective evidence and experience in implementing mental health solutions using digital phenotyping and smartphone app-based care delivery in India, we define the scope, potential, and challenges of implementing synchronous and asynchronous digital mental health solutions that can serve as a template for improving global mental health.

Multiparametric Cranberry (Vaccinium macrocarpon Ait.) Fruit Textural Trait Development for Harvest and Postharvest Evaluation in Representative Cultivars.

Fruit texture is a priority trait that guarantees the long-term economic sustainability of the cranberry industry through value-added products such as sweetened dried cranberries (SDCs). To develop a standard methodology to measure texture, we conducted a comparative analysis of 22 textural traits using five different methods under both harvest and postharvest conditions in 10 representative cranberry cultivars. A set of textural traits from the 10%-strain compression and puncture methods were identified that differentiate between cultivars primarily based on hardness/stiffness and elasticity properties. The complementary use of both methodologies allowed for a detailed evaluation by capturing the effect of key texture-determining factors such as structure, flesh, and skin. Furthermore, the high effectiveness of this approach in different conditions and its ability to capture high phenotypic variation in cultivars highlights its great potential for applicability in various areas of the value chain and research. Therefore, this study provides an informed reference for unifying future efforts to enhance cranberry fruit texture and quality.

Multi-class plant hormone HILIC-MS/MS analysis coupled with high-throughput phenotyping to investigate plant-environment interactions.

Plant hormones are chemical signals governing almost every aspect of a plant's life cycle and responses to environmental cues. They are enmeshed within complex signaling networks that can only be deciphered by using broad-scale analytical methods to capture information about several plant hormone classes simultaneously. Methods used for this purpose are all based on reversed-phase (RP) liquid chromatography and mass spectrometric detection. Hydrophilic interaction chromatography (HILIC) is an alternative chromatographic method that performs well in analyses of biological samples. We therefore developed and validated a HILIC method for broad-scale plant hormone analysis including a rapid sample preparation procedure; moreover, derivatization or fractionation is not required. The method enables plant hormone screening focused on polar and moderately polar analytes including cytokinins, auxins, jasmonates, abscisic acid and its metabolites, salicylates, indoleamines (melatonin), and 1-aminocyclopropane-1-carboxylic acid (ACC), for a total of 45 analytes. Importantly, the major pitfalls of ACC analysis have been addressed. Furthermore, HILIC provides orthogonal selectivity to conventional RP methods and displays greater sensitivity, resulting in lower limits of quantification. However, it is less robust, so procedures to increase its reproducibility were established. The method's potential is demonstrated in a case study by employing an approach combining hormonal analysis with phenomics to examine responses of three Arabidopsis ecotypes toward three abiotic stress treatments: salinity, low nutrient availability, and their combination. The case study showcases the value of the simultaneous determination of several plant hormone classes coupled with phenomics data when unraveling processes involving complex cross-talk under diverse plant-environment interactions.