Large language models facilitate the generation of electronic health record phenotyping algorithms

OBJECTIVES

Phenotyping is a core task in observational health research utilizing electronic health records (EHRs). Developing an accurate algorithm demands substantial input from domain experts, involving extensive literature review and evidence synthesis. This burdensome process limits scalability and delays knowledge discovery. We investigate the potential for leveraging large language models (LLMs) to enhance the efficiency of EHR phenotyping by generating high-quality algorithm drafts.

MATERIALS AND METHODS

We prompted four LLMs-GPT-4 and GPT-3.5 of ChatGPT, Claude 2, and Bard-in October 2023, asking them to generate executable phenotyping algorithms in the form of SQL queries adhering to a common data model (CDM) for three phenotypes (ie, type 2 diabetes mellitus, dementia, and hypothyroidism). Three phenotyping experts evaluated the returned algorithms across several critical metrics. We further implemented the top-rated algorithms and compared them against clinician-validated phenotyping algorithms from the Electronic Medical Records and Genomics (eMERGE) network.

RESULTS

GPT-4 and GPT-3.5 exhibited significantly higher overall expert evaluation scores in instruction following, algorithmic logic, and SQL executability, when compared to Claude 2 and Bard. Although GPT-4 and GPT-3.5 effectively identified relevant clinical concepts, they exhibited immature capability in organizing phenotyping criteria with the proper logic, leading to phenotyping algorithms that were either excessively restrictive (with low recall) or overly broad (with low positive predictive values).

CONCLUSION

GPT versions 3.5 and 4 are capable of drafting phenotyping algorithms by identifying relevant clinical criteria aligned with a CDM. However, expertise in informatics and clinical experience is still required to assess and further refine generated algorithms.

Deep Phenotyping the Anterior Urethral Stricture: Characterizing the Relationship Between Inflammation, Fibrosis, Patient History and Disease Pathophysiology

INTRODUCTION

Anterior urethral stricture disease (aUSD) is a complex, heterogeneous condition which is idiopathic in origin for most men. This gap in knowledge rarely affects the current management strategy for aUSD, as urethroplasty does not generally consider etiology. However, as we transition towards personalized, minimally invasive treatments for aUSD and begin to consider aUSD prevention strategies, disease pathophysiology will become increasingly important. The purpose of this study was to perform a deep phenotype of men undergoing anterior urethroplasty for aUSD. We hypothesized that unique biologic signatures and potential targets for intervention would emerge based on stricture presence/absence, stricture etiology, and the presence/absence of stricture inflammation.

MATERIALS/METHODS

Men with aUSD undergoing urethroplasty were recruited from one of five participating centers. Enrollees provided urethral stricture tissue and blood/serum on the day of surgery and completed patient reported outcome measure questionnaires both pre and post-operatively. The initial study had three aims: (1) to determine pediatric and adult subacute and repeated perineal trauma (SRPT) exposures using a study-specific SRPT questionnaire (2) to determine the degree of inflammation and fibrosis in aUSD and peri-aUSD (normal urethra) tissue and (3) to determine levels of systemic inflammatory and fibrotic cytokines. Two controls groups provided serum (normal vasectomy patients) and urethral tissue (autopsy patients). Cohorts were based on the presence/absence of stricture, by presumed stricture etiology (idiopathic, traumatic/iatrogenic, lichen sclerosus [LS]), and by the presence/absence of stricture inflammation.

RESULTS

Of 138 enrolled men (120 tissue/serum; 18 stricture tissue only), 78 had idiopathic strictures, 33 had trauma-related strictures, and 27 had LS-related strictures. BMI, stricture length, and stricture location significantly differed between cohorts (P < .001 for each). The highest BMIs and the longest strictures were observed in the LS cohort. SRPT exposures did not significantly differ between etiology cohorts, with > 60% of each reporting low/mild risk. Stricture inflammation significantly differed between cohorts, with mild to severe inflammation present in 27% of trauma-related strictures, 54% of idiopathic strictures, and 48% of LS strictures (P = 0.036). Stricture fibrosis did not significantly differ between cohorts (P = .7). Three serum cytokines were significantly higher in patients with strictures compared to stricture-free controls: IL-9 (P = .001), PDGF-BB (P = 0.004), and CCL5 (P = .01). No differences were observed in the levels of these cytokines based on stricture etiology. However, IL-9 levels were significantly higher in patients with inflamed strictures than in patients with strictures lacking inflammation (P = .019). Degree of stricture inflammation positively correlated with serum levels of IL-9 (Spearman's rho 0.224, P = .014).

CONCLUSION

The most common aUSD etiology is idiopathic. Though convention has implicated SRPT as causative for idiopathic strictures, here we found that patients with idiopathic strictures had low SRPT rates that were similar to rates in patients with a known stricture etiology. Stricture and stricture-adjacent inflammation in idiopathic stricture were similar to LS strictures, suggesting shared pathophysiologic mechanisms. IL-9, PDGF-BB and CCL5, which were elevated patients with strictures, have been implicated in fibrotic conditions elsewhere in the body. Further work will be required to determine if this shared biologic signature represents a potential mechanism for an aUSD predisposition.

Broadening the ocular phenotypic spectrum of ultra-rare BRPF1 variants: report of two cases

INTRODUCTION

BRPF1 gene on 3p26-p25 encodes a protein involved in epigenetic regulation, through interaction with histone H3 lysine acetyltransferases KAT6A and KAT6B of the MYST family. Heterozygous pathogenic variants in BRPF1 gene are associated with Intellectual Developmental Disorder with Dysmorphic Facies and Ptosis (IDDDFP), characterized by global developmental delay, intellectual disability, language delay, and dysmorphic facial features. The reported ocular involvement includes strabismus, amblyopia, and refraction errors. This report describes a novel ocular finding in patients affected by variants in the BRPF1 gene.

METHODS

We performed exome sequencing and deep ocular phenotyping in two unrelated patients (P1, P2) with mild intellectual disability, ptosis, and typical facies.

RESULTS

Interestingly, P1 had a Chiari Malformation type I and a subclinical optic neuropathy, which could not be explained by variations in other genes. Having detected a peculiar ocular phenotype in P1, we suggested optical coherence tomography (OCT) for P2; such an exam also detected bilateral subclinical optic neuropathy in this case.

DISCUSSION

To date, only a few patients with BRPF1 variants have been described, and none were reported to have optic neuropathy. Since subclinical optic nerve alterations can go easily undetected, our experience highlights the importance of a more detailed ophthalmologic evaluation in patients with BRPF1 variant.

Improving reporting standards for phenotyping algorithm in biomedical research: 5 fundamental dimensions

INTRODUCTION

Phenotyping algorithms enable the interpretation of complex health data and definition of clinically relevant phenotypes; they have become crucial in biomedical research. However, the lack of standardization and transparency inhibits the cross-comparison of findings among different studies, limits large scale meta-analyses, confuses the research community, and prevents the reuse of algorithms, which results in duplication of efforts and the waste of valuable resources.

RECOMMENDATIONS

Here, we propose five independent fundamental dimensions of phenotyping algorithms-complexity, performance, efficiency, implementability, and maintenance-through which researchers can describe, measure, and deploy any algorithms efficiently and effectively. These dimensions must be considered in the context of explicit use cases and transparent methods to ensure that they do not reflect unexpected biases or exacerbate inequities.

Bridging information gaps in menopause status classification through natural language processing

Objective

To use natural language processing (NLP) of clinical notes to augment existing structured electronic health record (EHR) data for classification of a patient's menopausal status.

Materials and methods

A rule-based NLP system was designed to capture evidence of a patient's menopause status including dates of a patient's last menstrual period, reproductive surgeries, and postmenopause diagnosis as well as their use of birth control and menstrual interruptions. NLP-derived output was used in combination with structured EHR data to classify a patient's menopausal status. NLP processing and patient classification were performed on a cohort of 307 512 female Veterans receiving healthcare at the US Department of Veterans Affairs (VA).

Results

NLP was validated at 99.6% precision. Including the NLP-derived data into a menopause phenotype increased the number of patients with data relevant to their menopausal status by 118%. Using structured codes alone, 81 173 (27.0%) are able to be classified as postmenopausal or premenopausal. However, with the inclusion of NLP, this number increased 167 804 (54.6%) patients. The premenopausal category grew by 532.7% with the inclusion of NLP data.

Discussion

By employing NLP, it became possible to identify documented data elements that predate VA care, originate outside VA networks, or have no corresponding structured field in the VA EHR that would be otherwise inaccessible for further analysis.

Conclusion

NLP can be used to identify concepts relevant to a patient's menopausal status in clinical notes. Adding NLP-derived data to an algorithm classifying a patient's menopausal status significantly increases the number of patients classified using EHR data, ultimately enabling more detailed assessments of the impact of menopause on health outcomes.

Digital health applications for eating disorders treatment

Eating disorders (EDs) are characterized by multifaceted etiologies, difficulties in accessing care (especially in regional locations), and variable responsiveness to treatments. Digital technologies are viewed as an important innovation in the assessment and treatment of EDs. We discuss current implementation of these innovations as well as important future directions for the field.

SPIRO - the automated Petri plate imaging platform designed by biologists, for biologists

Phenotyping of model organisms grown on Petri plates is often carried out manually, despite the procedures being time-consuming and laborious. The main reason for this is the limited availability of automated phenotyping facilities, whereas constructing a custom automated solution can be a daunting task for biologists. Here, we describe SPIRO, the Smart Plate Imaging Robot, an automated platform that acquires time-lapse photographs of up to four vertically oriented Petri plates in a single experiment, corresponding to 192 seedlings for a typical root growth assay and up to 2500 seeds for a germination assay. SPIRO is catered specifically to biologists' needs, requiring no engineering or programming expertise for assembly and operation. Its small footprint is optimized for standard incubators, the inbuilt green LED enables imaging under dark conditions, and remote control provides access to the data without interfering with sample growth. SPIRO's excellent image quality is suitable for automated image processing, which we demonstrate on the example of seed germination and root growth assays. Furthermore, the robot can be easily customized for specific uses, as all information about SPIRO is released under open-source licenses. Importantly, uninterrupted imaging allows considerably more precise assessment of seed germination parameters and root growth rates compared with manual assays. Moreover, SPIRO enables previously technically challenging assays such as phenotyping in the dark. We illustrate the benefits of SPIRO in proof-of-concept experiments which yielded a novel insight on the interplay between autophagy, nitrogen sensing, and photoblastic response.

Three-dimensional fruit growth analysis clarifies developmental mechanisms underlying complex shape diversity in persimmon fruit

The determination of fruit size and shape are of considerable interest in horticulture and developmental biology. Fruit typically exhibits three-dimensional structures characterized by geometric features that are dependent on the genotype. Although minor developmental variations have been recognized, few studies have fully visualized and measured these variations throughout fruit growth. Here, a high-resolution 3D scanner was used to investigate the fruit development of 51 persimmon (Diospyros kaki) cultivars with various complex shapes. We obtained 2380 3D models that fully represented fruit appearance, and enabled precise and automated measurements of shape features throughout fruit development, including horizontal and vertical grooves, length-to-width ratio, and roundness. The 3D fruit model analysis identified key stages that determined the shape attributes at maturity. Typically, genetic diversity was found in vertical groove development, and these grooves could be filled by tissue expansion in the carpel fusion zone during fruit development. In addition, transcriptome analysis of fruit tissues from groove and non-groove tissues revealed gene co-expression networks that were highly associated with groove depth variation. The presence of YABBY homologs was most closely associated with groove depth and indicated the possibility that this pathway is a key molecular contributor to vertical groove depth variation. Overall, our results revealed deterministic patterns of complex shape traits in persimmon fruit and showed that different growth patterns among tissues are the main factor contributing to the shape of both vertical and horizontal grooves.

Phenotypes and Serum Biomarkers in Sarcoidosis

Sarcoidosis is a multisystem disease, which is diagnosed on a compatible clinical presentation, non-necrotizing granulomatous inflammation in one or more tissue samples, and exclusion of alternative causes of granulomatous disease. Considering its heterogeneity, numerous aspects of the disease remain to be elucidated. In this context, the identification and integration of biomarkers may hold significance in clinical practice, aiding in appropriate selection of patients for targeted clinical trials. This work aims to discuss and analyze how validated biomarkers are currently integrated in disease category definitions. Future studies are mandatory to unravel the diverse contributions of genetics, socioeconomic status, environmental exposures, and other sociodemographic variables to disease severity and phenotypic presentation. Furthermore, the implementation of transcriptomics, multidisciplinary approaches, and consideration of patients' perspectives, reporting innovative insights, could be pivotal for a better understanding of disease pathogenesis and the optimization of clinical assistance.

Novel loci associated with resistance to downy and powdery mildew in grapevine

Among the main challenges in current viticulture, there is the increasing demand for sustainability in the protection from fungal diseases, such as downy mildew (DM) and powdery mildew (PM). Breeding disease-resistant grapevine varieties is a key strategy for better managing fungicide inputs. This study explores the diversity of grapevine germplasm (cultivated and wild) from Caucasus and neighboring areas to identify genotypes resistant to DM and PM, based on 13 Simple Sequence Repeat (SSR) loci and phenotypical (artificial pathogen inoculation) analysis, and to identify loci associated with DM and PM resistance, via Genome-Wide Association Analysis (GWAS) on Single Nucleotide Polymorphism (SNP) profiles. SSR analysis revealed resistant alleles for 16 out of 88 genotypes. Phenotypic data identified seven DM and 31 PM resistant genotypes. GWAS identified two new loci associated with DM resistance, located on chromosome 15 and 16 (designated as Rpv36 and Rpv37), and two with PM resistance, located on chromosome 6 and 17 (designated as Ren14 and Ren15). The four novel loci identified genomic regions rich in genes related to biotic stress response, such as genes involved in pathogen recognition, signal transduction and resistance response. This study highlights potential candidate genes associated with resistance to DM and PM, providing valuable insights for breeding programs for resistant varieties. To optimize their utilization, further functional characterization studies are recommended.

A Human Brain Map of Mitochondrial Respiratory Capacity and Diversity

Mitochondrial oxidative phosphorylation (OxPhos) powers brain activity1,2, and mitochondrial defects are linked to neurodegenerative and neuropsychiatric disorders3,4, underscoring the need to define the brain's molecular energetic landscape5-10. To bridge the cognitive neuroscience and cell biology scale gap, we developed a physical voxelization approach to partition a frozen human coronal hemisphere section into 703 voxels comparable to neuroimaging resolution (3×3×3 mm). In each cortical and subcortical brain voxel, we profiled mitochondrial phenotypes including OxPhos enzyme activities, mitochondrial DNA and volume density, and mitochondria-specific respiratory capacity. We show that the human brain contains a diversity of mitochondrial phenotypes driven by both topology and cell types. Compared to white matter, grey matter contains >50% more mitochondria. We show that the more abundant grey matter mitochondria also are biochemically optimized for energy transformation, particularly among recently evolved cortical brain regions. Scaling these data to the whole brain, we created a backward linear regression model integrating several neuroimaging modalities11, thereby generating a brain-wide map of mitochondrial distribution and specialization that predicts mitochondrial characteristics in an independent brain region of the same donor brain. This new approach and the resulting MitoBrainMap of mitochondrial phenotypes provide a foundation for exploring the molecular energetic landscape that enables normal brain functions, relating it to neuroimaging data, and defining the subcellular basis for regionalized brain processes relevant to neuropsychiatric and neurodegenerative disorders.

World Endometriosis Research Foundation Endometriosis Phenome and Biobanking Harmonization Project: V. physical examination standards in endometriosis research

OBJECTIVE

The World Endometriosis Research Foundation (WERF) established the Endometriosis Phenome and Biobanking Harmonisation Project (EPHect) to create standardized documentation tools (with common data elements) to facilitate the comparison and combination of data across different research sites and studies. In 2014, four data research standards were published: clinician-reported surgical data, patient-reported clinical data, and fluid and tissue biospecimen collection. Our current objective is to create an EPHect standard for the clinician-reported physical examination (EPHect-PE) for research studies.

DESIGN

An international consortium involving 26 clinical and academic experts and patient partners from 11 countries representing 25 institutions and organizations. Two virtual workshops, followed by the development of the physical examination standards that underwent multiple rounds of iterations and revisions.

SUBJECTS

N/A MAIN OUTCOME MEASURE(S): N/A RESULT(S): The EPHect physical examination (EPHect-PE) tool provides standardised assessment of physical examination characteristics and pain phenotyping. Data elements involve examination of a) back and pelvic girdle; b) abdomen including allodynia and trigger points; c) vulva including provoked vestibulodynia; d) pelvic floor muscle tone and tenderness; e) tenderness on unidigital pelvic exam; f) presence of pelvic nodularity; g) uterine size and mobility; h) presence of adnexal masses; i) presence of incisional masses; j) speculum examination; k) tenderness and allodynia at an extra-pelvic site (e.g. forearm); and l) recording of anthropometrics.

CONCLUSION(S)

The EPHect physical examination standards (EPHect-PE) will facilitate the standardised documentation of the physical examination, including the assessment and documentation of examination phenotyping of endometriosis-associated pelvic pain.

Healthcare utilization and clinical characteristics of genetic epilepsy in electronic health records

Understanding the clinical characteristics and medical treatment of individuals affected by genetic epilepsies is instrumental in guiding selection for genetic testing, defining the phenotype range of these rare disorders, optimizing patient care pathways and pinpointing unaddressed medical need by quantifying healthcare resource utilization. To date, a matched longitudinal cohort study encompassing the entire spectrum of clinical characteristics and medical treatment from childhood through adolescence has not been performed. We identified individuals with genetic and non-genetic epilepsies and onset at ages 0-5 years by linkage across the Cleveland Clinic Health System. We used natural language processing to extract medical terms and procedures from longitudinal electronic health records and tested for cross-sectional and temporal associations with genetic epilepsy. We implemented a two-stage design: in the discovery cohort, individuals were stratified as being 'likely genetic' or 'non-genetic' by a natural language processing algorithm, and controls did not receive genetic testing. The validation cohort consisted of cases with genetic epilepsy confirmed by manual chart review and an independent set of controls who received negative genetic testing. The discovery and validation cohorts consisted of 503 and 344 individuals with genetic epilepsy and matched controls, respectively. The median age at the first encounter was 0.1 years and 7.9 years at the last encounter, and the mean duration of follow-up was 8.2 years. We extracted 188,295 Unified Medical Language System annotations for statistical analysis across 9659 encounters. Individuals with genetic epilepsy received an earlier epilepsy diagnosis and had more frequent and complex encounters with the healthcare system. Notably, the highest enrichment of encounters compared with the non-genetic groups was found during the transition from paediatric to adult care. Our computational approach could validate established comorbidities of genetic epilepsies, such as behavioural abnormality and intellectual disability. We also revealed novel associations for genitourinary abnormalities (odds ratio 1.91, 95% confidence interval: 1.66-2.20, P = 6.16 × 10-19) linked to a spectrum of underrecognized epilepsy-associated genetic disorders. This case-control study leveraged real-world data to identify novel features associated with the likelihood of a genetic aetiology and quantified the healthcare utilization of genetic epilepsies compared with matched controls. Our results strongly recommend early genetic testing to stratify individuals into specialized care paths, thus improving the clinical management of people with genetic epilepsies.

Protein hydrolysates enhance recovery from drought stress in tomato plants: phenomic and metabolomic insights

Introduction

High-throughput phenotyping technologies together with metabolomics analysis can speed up the development of highly efficient and effective biostimulants for enhancing crop tolerance to drought stress. The aim of this study was to examine the morphophysiological and metabolic changes in tomato plants foliarly treated with two protein hydrolysates obtained by enzymatic hydrolysis of vegetal proteins from Malvaceae (PH1) or Fabaceae (PH2) in comparison with a control treatment, as well as to investigate the mechanisms involved in the enhancement of plant resistance to repeated drought stress cycles.

Methods

A phenotyping device was used for daily monitoring morphophysiological traits while untargeted metabolomics analysis was carried out in leaves of the best performing treatment based on phenotypic results.Results: PH1 treatment was the most effective in enhancing plant resistance to water stress due to the better recovery of digital biomass and 3D leaf area after each water stress event while PH2 was effective in mitigating water stress only during the recovery period after the first drought stress event. Metabolomics data indicated that PH1 modified primary metabolism by increasing the concentration of dipeptides and fatty acids in comparison with untreated control, as well as secondary metabolism by regulating several compounds like phenols. In contrast, hormones and compounds involved in detoxification or signal molecules against reactive oxygen species were downregulated in comparison with untreated control.

Conclusion

The above findings demonstrated the advantages of a combined phenomics-metabolomics approach for elucidating the relationship between metabolic and morphophysiological changes associated with a biostimulant-mediated increase of crop resistance to repeated water stress events.

A Human Brain Map of Mitochondrial Respiratory Capacity and Diversity

Mitochondrial oxidative phosphorylation (OxPhos) powers brain activity1,2, and mitochondrial defects are linked to neurodegenerative and neuropsychiatric disorders3,4, underscoring the need to define the brain's molecular energetic landscape5-10. To bridge the cognitive neuroscience and cell biology scale gap, we developed a physical voxelization approach to partition a frozen human coronal hemisphere section into 703 voxels comparable to neuroimaging resolution (3×3×3 mm). In each cortical and subcortical brain voxel, we profiled mitochondrial phenotypes including OxPhos enzyme activities, mitochondrial DNA and volume density, and mitochondria-specific respiratory capacity. We show that the human brain contains a diversity of mitochondrial phenotypes driven by both topology and cell types. Compared to white matter, grey matter contains >50% more mitochondria. We show that the more abundant grey matter mitochondria also are biochemically optimized for energy transformation, particularly among recently evolved cortical brain regions. Scaling these data to the whole brain, we created a backward linear regression model integrating several neuroimaging modalities11, thereby generating a brain-wide map of mitochondrial distribution and specialization that predicts mitochondrial characteristics in an independent brain region of the same donor brain. This new approach and the resulting MitoBrainMap of mitochondrial phenotypes provide a foundation for exploring the molecular energetic landscape that enables normal brain functions, relating it to neuroimaging data, and defining the subcellular basis for regionalized brain processes relevant to neuropsychiatric and neurodegenerative disorders.

Transferability of bone phenotyping and fracture risk assessment by μFRAC from first-generation high-resolution peripheral quantitative computed tomography to second-generation scan data

INTRODUCTION

The continued development of high-resolution peripheral quantitative computed tomography (HR-pQCT) has led to a second-generation scanner with higher resolution and longer scan region. However, large multi-center prospective cohorts were collected with first-generation HR-pQCT and have been used to develop bone phenotyping and fracture risk prediction (μFRAC) models. This study establishes whether there is sufficient universality of these first-generation trained models for use with second-generation scan data.

METHODS

HR-pQCT data was collected for a cohort of 60 individuals, who had been scanned on both first- and second-generation scanners on the same day to establish the universality of the HR-pQCT models. These data were each used as input to first-generation trained bone microarchitecture models for bone phenotyping and fracture risk prediction, and their outputs were compared for each study participant. Reproducibility of the models were assessed using same-day repeat scans obtained from first-generation (n = 37) and second-generation (n = 74) scanners.

RESULTS

Across scanner generations, the bone phenotyping model performed with an accuracy of 93.1%. Similarly, the five-year fracture risk assessment by μFRAC was well correlated with a Pearson's (r) correlation coefficient of r > 0.83 for the three variations of μFRAC (varying inclusion of clinical risk factors, finite element analysis, and dual X-ray absorptiometry). The first-generation reproducibility cohort performed with an accuracy for categorical assignment of 100% (bone phenotyping), and a correlation coefficient of 0.99 (μFRAC), the second-generation reproducibility cohort performed with an accuracy of 96.4% (bone phenotyping), and a correlation coefficient of 0.99 (μFRAC).

CONCLUSION

We demonstrated that bone microarchitecture models trained using first-generation scan data generalize well to second-generation scans, performing with a high level of accuracy and reproducibility. Less than 4% of individuals' estimated fracture risk led to a change in treatment threshold, and in general these dissimilar outcomes using second-generation data tended to be more conservative.

Advances and Challenges in Sepsis Management: Modern Tools and Future Directions

Sepsis, a critical condition marked by systemic inflammation, profoundly impacts both innate and adaptive immunity, often resulting in lymphopenia. This immune alteration can spare regulatory T cells (Tregs) but significantly affects other lymphocyte subsets, leading to diminished effector functions, altered cytokine profiles, and metabolic changes. The complexity of sepsis stems not only from its pathophysiology but also from the heterogeneity of patient responses, posing significant challenges in developing universally effective therapies. This review emphasizes the importance of phenotyping in sepsis to enhance patient-specific diagnostic and therapeutic strategies. Phenotyping immune cells, which categorizes patients based on clinical and immunological characteristics, is pivotal for tailoring treatment approaches. Flow cytometry emerges as a crucial tool in this endeavor, offering rapid, low cost and detailed analysis of immune cell populations and their functional states. Indeed, this technology facilitates the understanding of immune dysfunctions in sepsis and contributes to the identification of novel biomarkers. Our review underscores the potential of integrating flow cytometry with omics data, machine learning and clinical observations to refine sepsis management, highlighting the shift towards personalized medicine in critical care. This approach could lead to more precise interventions, improving outcomes in this heterogeneously affected patient population.

Left Ventricular Ejection Fraction and the Future of Heart Failure Phenotyping

Heart failure (HF) is a complex syndrome traditionally classified by left ventricular ejection fraction (LVEF) cutpoints. Although LVEF is prognostic for risk of events and predictive of response to some HF therapies, LVEF is a continuous variable and cutpoints are arbitrary, often based on historical clinical trial enrichment decisions rather than physiology. Holistic evaluation of the treatment effects for therapies throughout the LVEF range suggests the standard categorization paradigm for HF merits modification. The multidisciplinary Heart Failure Collaboratory reviewed data from large-scale HF clinical trials and found that many HF therapies have demonstrated therapeutic benefit across a large range of LVEF, but specific treatment effects vary across that range. Therefore, HF should practically be classified by association with an LVEF that is reduced or not reduced, while acknowledging uncertainty around the precise LVEF cutpoint, and future research should evaluate new therapies across the continuum of LVEF.

Evaluation of daytime sleepiness and insomnia symptoms in OSA patients with a characterization of symptom-defined phenotypes and their involvement in depression comorbidity-a cross-sectional clinical study

Introduction

Recent research highlights the significance of insomnia and sleepiness, shifting from obstructive sleep apnea (OSA) severity and sleep structure, in defining OSA phenotypes.

Objectives

This study aimed to characterize insomnia and sleepiness associated with OSA phenotypes and assess their involvement in depression symptoms (DS) in OSA.

Materials and methods

This cross-sectional, clinical study included 181 participants who underwent polysomnography (PSG) and filled out questionnaires, including the Epworth Sleepiness Scale (ESS), Insomnia Severity Index (ISI), Pittsburgh Sleep Quality Index (PSQI), and Beck Depression Index (BDI). They were categorized into phenotypes: insomnia-sleepiness (I + S; ESS ≥ 11; ISI ≥ 15; n = 20), sleepiness (S; ESS ≥ 11; ISI < 15; n = 22), insomnia (I; ESS < 11; ISI ≥ 15), and asymptomatic (A; ESS < 11; ISI<15; n=55).

Results

A linear regression model for the BDI score (R2 = 0.357, p < 0.001) included ISI score and subjective-to-objective sleep latency ratio. The ISI score was a predictive factor for mild and moderate DS [OR = 1.23 (95% CI: 1.09-1.38), p < 0.001 and OR = 1.39 (95% CI: 1.13-1.72), p = 0.002]. The I and I + S phenotypes are characterized by higher BDI scores (p < 0.001 and p = 0.02), longer subjective sleep latency (p = 0.008 and p = 0.04), and shorter subjective total sleep time (TST; p = 0.049 and p = 0.006) compared to A. Furthermore, the I and I + S groups had shorter subjective TST than S (p = 0.03 and p = 0.047). The I and I + S had higher BDI scores than A (p < 0.001 and p = 0.02, respectively) and S (p < 0.001 and p = 0.02, respectively). The I phenotype was associated with the risk of mild and moderate DS (OR = 5.61 (95% CI: 1.91-16.53), p < 0.001 and OR = 9.55 (95% CI: 1.81-50.48), p = 0.008 respectively). Moreover, the I + S phenotype presented an even greater risk for mild DS (OR = 10.29 (95% CI: 2.95-35.85), p < 0.001).

Conclusion

Using clinical features for OSA phenotyping holds promise for finding OSA individuals with increased risk for DS occurrence.

Evidence for solanidine as a dietary CYP2D6 biomarker: Significant correlation with risperidone metabolism

AIMS

The extensive variability in cytochrome P450 2D6 (CYP2D6) metabolism is mainly caused by genetic polymorphisms. However, there is large, unexplained variability in CYP2D6 metabolism within CYP2D6 genotype subgroups. Solanidine, a dietary compound found in potatoes, is a promising phenotype biomarker predicting individual CYP2D6 metabolism. The aim of this study was to investigate the correlation between solanidine metabolism and the CYP2D6-mediated metabolism of risperidone in patients with known CYP2D6 genotypes.

METHODS

The study included therapeutic drug monitoring (TDM) data from CYP2D6-genotyped patients treated with risperidone. Risperidone and 9-hydroxyrisperidone levels were determined during TDM, and reprocessing of the respective TDM full-scan high-resolution mass spectrometry files was applied for semi-quantitative measurements of solanidine and five metabolites (M402, M414, M416, M440 and M444). Spearman's tests determined the correlations between solanidine metabolic ratios (MRs) and the 9-hydroxyrisperidone-to-risperidone ratio.

RESULTS

A total of 229 patients were included. Highly significant, positive correlationswere observed between all solanidine MRs and the 9-hydroxyrisperidone-to-risperidone ratio (ρ > 0.6, P < .0001). The strongest correlation was observed for the M444-to-solanidine MR in patients with functional CYP2D6 metabolism, i.e., genotype activity scores of 1 and 1.5 (ρ 0.72-0.77, P < .0001).

CONCLUSION

The present study shows strong, positive correlations between solanidine metabolism and CYP2D6-mediated risperidone metabolism. The strong correlation within patients carrying CYP2D6 genotypes encoding functional CYP2D6 metabolism suggests that solanidine metabolism may predict individual CYP2D6 metabolism, and hence potentially improve personalized dosing of drugs metabolized by CYP2D6.

Phenotyping of Potato Plants Using Morphological and Physiological Tools

Potato (Solanum tuberosum L.) is one of the main non-grain agricultural crops and one of the main sources of food for humanity. Currently, growing potatoes requires new approaches and methods for cultivation and breeding. Phenotyping is one of the important tools for assessing the characteristics of a potato variety. In this work, 29 potato varieties of different ripeness groups were studied. Linear leaf dimensions, leaf mass area, number of stems, number of tubers per plant, average tuber weight, signs of virus infection, dry weight, pigment content, and number of stomata per unit leaf area were used as phenotyping tools. The strongest positive relationship was found between yield and bush area in the stage of full shoots (R = 0.77, p = 0.001), linear dimensions of a complex leaf (R = 0.44, p = 0.002; R = 0.40, p = 0.003), number of stems (R = 0.36, p = 0.05), and resistance to viruses X (R = 0.42, p = 0.03) and S (R = 0.43, p = 0.02). An inverse relationship was found between growth dynamics and yield (R = -0.29, p = 0.05). Thus, the use of morphological and physiological phenotyping tools in the field is informative for predicting key agricultural characteristics such as yield and/or stress resistance.

Corrigendum: Proximal hyperspectral imaging detects diurnal and drought-induced changes in maize physiology

[This corrects the article DOI: 10.3389/fpls.2021.640914.].

Semi-supervised ROC analysis for reliable and streamlined evaluation of phenotyping algorithms

OBJECTIVE

High-throughput phenotyping will accelerate the use of electronic health records (EHRs) for translational research. A critical roadblock is the extensive medical supervision required for phenotyping algorithm (PA) estimation and evaluation. To address this challenge, numerous weakly-supervised learning methods have been proposed. However, there is a paucity of methods for reliably evaluating the predictive performance of PAs when a very small proportion of the data is labeled. To fill this gap, we introduce a semi-supervised approach (ssROC) for estimation of the receiver operating characteristic (ROC) parameters of PAs (eg, sensitivity, specificity).

MATERIALS AND METHODS

ssROC uses a small labeled dataset to nonparametrically impute missing labels. The imputations are then used for ROC parameter estimation to yield more precise estimates of PA performance relative to classical supervised ROC analysis (supROC) using only labeled data. We evaluated ssROC with synthetic, semi-synthetic, and EHR data from Mass General Brigham (MGB).

RESULTS

ssROC produced ROC parameter estimates with minimal bias and significantly lower variance than supROC in the simulated and semi-synthetic data. For the 5 PAs from MGB, the estimates from ssROC are 30% to 60% less variable than supROC on average.

DISCUSSION

ssROC enables precise evaluation of PA performance without demanding large volumes of labeled data. ssROC is also easily implementable in open-source R software.

CONCLUSION

When used in conjunction with weakly-supervised PAs, ssROC facilitates the reliable and streamlined phenotyping necessary for EHR-based research.

Data-driven automated classification algorithms for acute health conditions: applying PheNorm to COVID-19 disease

OBJECTIVES

Automated phenotyping algorithms can reduce development time and operator dependence compared to manually developed algorithms. One such approach, PheNorm, has performed well for identifying chronic health conditions, but its performance for acute conditions is largely unknown. Herein, we implement and evaluate PheNorm applied to symptomatic COVID-19 disease to investigate its potential feasibility for rapid phenotyping of acute health conditions.

MATERIALS AND METHODS

PheNorm is a general-purpose automated approach to creating computable phenotype algorithms based on natural language processing, machine learning, and (low cost) silver-standard training labels. We applied PheNorm to cohorts of potential COVID-19 patients from 2 institutions and used gold-standard manual chart review data to investigate the impact on performance of alternative feature engineering options and implementing externally trained models without local retraining.

RESULTS

Models at each institution achieved AUC, sensitivity, and positive predictive value of 0.853, 0.879, 0.851 and 0.804, 0.976, and 0.885, respectively, at quantiles of model-predicted risk that maximize F1. We report performance metrics for all combinations of silver labels, feature engineering options, and models trained internally versus externally.

DISCUSSION

Phenotyping algorithms developed using PheNorm performed well at both institutions. Performance varied with different silver-standard labels and feature engineering options. Models developed locally at one site also worked well when implemented externally at the other site.

CONCLUSION

PheNorm models successfully identified an acute health condition, symptomatic COVID-19. The simplicity of the PheNorm approach allows it to be applied at multiple study sites with substantially reduced overhead compared to traditional approaches.

Identifying clinical phenotypes of frontotemporal dementia in post-9/11 era veterans using natural language processing

Introduction

Frontotemporal dementia (FTD) encompasses a clinically and pathologically diverse group of neurodegenerative disorders, yet little work has quantified the unique phenotypic clinical presentations of FTD among post-9/11 era veterans. To identify phenotypes of FTD using natural language processing (NLP) aided medical chart reviews of post-9/11 era U.S. military Veterans diagnosed with FTD in Veterans Health Administration care.

Methods

A medical record chart review of clinician/provider notes was conducted using a Natural Language Processing (NLP) tool, which extracted features related to cognitive dysfunction. NLP features were further organized into seven Research Domain Criteria Initiative (RDoC) domains, which were clustered to identify distinct phenotypes.

Results

Veterans with FTD were more likely to have notes that reflected the RDoC domains, with cognitive and positive valence domains showing the greatest difference across groups. Clustering of domains identified three symptom phenotypes agnostic to time of an individual having FTD, categorized as Low (16.4%), Moderate (69.2%), and High (14.5%) distress. Comparison across distress groups showed significant differences in physical and psychological characteristics, particularly prior history of head injury, insomnia, cardiac issues, anxiety, and alcohol misuse. The clustering result within the FTD group demonstrated a phenotype variant that exhibited a combination of language and behavioral symptoms. This phenotype presented with manifestations indicative of both language-related impairments and behavioral changes, showcasing the coexistence of features from both domains within the same individual.

Discussion

This study suggests FTD also presents across a continuum of severity and symptom distress, both within and across variants. The intensity of distress evident in clinical notes tends to cluster with more co-occurring conditions. This examination of phenotypic heterogeneity in clinical notes indicates that sensitivity to FTD diagnosis may be correlated to overall symptom distress, and future work incorporating NLP and phenotyping may help promote strategies for early detection of FTD.

Comparison of the Antibiotic Resistance of Escherichia coli Populations from Water and Biofilm in River Environments

Antibiotic-resistant, facultative pathogenic bacteria are commonly found in surface water; however, the factors influencing the spread and stabilization of antibiotic resistance in this habitat, particularly the role of biofilms, are not fully understood. The extent to which bacterial populations in biofilms or sediments exacerbate the problem for specific antibiotic classes or more broadly remains unanswered. In this study, we investigated the differences between the bacterial populations found in the surface water and sediment/biofilm of the Mur River and the Drava River in Austria. Samples of Escherichia coli were collected from both the water and sediment at two locations per river: upstream and downstream of urban areas that included a sewage treatment plant. The isolates were subjected to antimicrobial susceptibility testing against 21 antibiotics belonging to seven distinct classes. Additionally, isolates exhibiting either extended-spectrum beta-lactamase (ESBL) or carbapenemase phenotypes were further analyzed for specific antimicrobial resistance genes. E. coli isolates collected from all locations exhibited resistance to at least one of the tested antibiotics; on average, isolates from the Mur and Drava rivers showed 25.85% and 23.66% resistance, respectively. The most prevalent resistance observed was to ampicillin, amoxicillin-clavulanic acid, tetracycline, and nalidixic acid. Surprisingly, there was a similar proportion of resistant bacteria observed in both open water and sediment samples. The difference in resistance levels between the samples collected upstream and downstream of the cities was minimal. Out of all 831 isolates examined, 13 were identified as carrying ESBL genes, with 1 of these isolates also containing the gene for the KPC-2 carbapenemase. There were no significant differences between the biofilm (sediment) and open water samples in the occurrence of antibiotic resistance. For the E. coli populations in the examined rivers, the different factors in water and the sediment do not appear to influence the stability of resistance. No significant differences in antimicrobial resistance were observed between the bacterial populations collected from the biofilm (sediment) and open-water samples in either river. The different factors in water and the sediment do not appear to influence the stability of resistance. The minimal differences observed upstream and downstream of the cities could indicate that the river population already exhibits generalized resistance.

Linking photosynthesis and yield reveals a strategy to improve light use efficiency in a climbing bean breeding population

Photosynthesis drives plant physiology, biomass accumulation, and yield. Photosynthetic efficiency, specifically the operating efficiency of PSII (Fq'/Fm'), is highly responsive to actual growth conditions, especially to fluctuating photosynthetic photon fluence rate (PPFR). Under field conditions, plants constantly balance energy uptake to optimize growth. The dynamic regulation complicates the quantification of cumulative photochemical energy uptake based on the intercepted solar energy, its transduction into biomass, and the identification of efficient breeding lines. Here, we show significant effects on biomass related to genetic variation in photosynthetic efficiency of 178 climbing bean (Phaseolus vulgaris L.) lines. Under fluctuating conditions, the Fq'/Fm' was monitored throughout the growing period using hand-held and automated chlorophyll fluorescence phenotyping. The seasonal response of Fq'/Fm' to PPFR (ResponseG:PPFR) achieved significant correlations with biomass and yield, ranging from 0.33 to 0.35 and from 0.22 to 0.31 in two glasshouse and three field trials, respectively. Phenomic yield prediction outperformed genomic predictions for new environments in four trials under different growing conditions. Investigating genetic control over photosynthesis, one single nucleotide polymorphism (Chr09_37766289_13052) on chromosome 9 was significantly associated with ResponseG:PPFR in proximity to a candidate gene controlling chloroplast thylakoid formation. In conclusion, photosynthetic screening facilitates and accelerates selection for high yield potential.

Uncovering the essential roles of glutamate carboxypeptidase 2 orthologs in Caenorhabditis elegans

Human glutamate carboxypeptidase 2 (GCP2) from the M28B metalloprotease group is an important target for therapy in neurological disorders and an established tumor marker. However, its physiological functions remain unclear. To better understand general roles, we used the model organism Caenorhabditis elegans to genetically manipulate its three existing orthologous genes and evaluate the impact on worm physiology. The results of gene knockout studies showed that C. elegans GCP2 orthologs affect the pharyngeal physiology, reproduction, and structural integrity of the organism. Promoter-driven GFP expression revealed distinct localization for each of the three gene paralogs, with gcp-2.1 being most abundant in muscles, intestine, and pharyngeal interneurons, gcp-2.2 restricted to the phasmid neurons, and gcp-2.3 located in the excretory cell. The present study provides new insight into the unique phenotypic effects of GCP2 gene knockouts in C. elegans, and the specific tissue localizations. We believe that elucidation of particular roles in a non-mammalian organism can help to explain important questions linked to physiology of this protease group and in extension to human GCP2 involvement in pathophysiological processes.

Ensemble Clustering to Generate Phenotypes of Kidney Transplant Donors and Recipients

In this paper we investigate the generation of phenotypes for kidney transplant donors and recipients to assist with decision making around organ allocation. We present an ensemble clustering approach for multi-type data (numerical and categorical) using two different clustering approaches-i.e., model based and vector quantization based clustering. These clustering approaches were applied to a large, US national deceased donor kidney transplant recipient database to characterize members of each cluster (in an unsupervised fashion) and to determine whether the subsequent risk of graft failure differed for each cluster. We generated three distinct clusters of recipients, which were subsequently used to generate phenotypes. Each cluster phenotype had recipients with varying clinical features, and the risk of kidney transplant graft failure and mortality differed across clusters. Importantly, the clustering results by both approaches demonstrated a significant overlap. Utilization of two distinct clustering approaches may be a novel way to validate unsupervised clustering techniques and clustering can be used for organ allocation decision making on the basis of differential outcomes.

Design and Application of Multi-Center Clinical Research Platform for Phenotyping of Voriconazole Hepatotoxicity

We introduce a phenotyping pipeline for voriconazole hepatotoxicity based on a multi-center clinical research platform. Using the platform's queue construction, feature generation, and feature screening functions, 52 features were obtained for model training. The prediction model of voriconazole hepatotoxicity was obtained by using the model training and evaluation functions of the platform. Important risk factors and protection factors of the model were listed.

Developing and evaluating pediatric phecodes (Peds-Phecodes) for high-throughput phenotyping using electronic health records

OBJECTIVE

Pediatric patients have different diseases and outcomes than adults; however, existing phecodes do not capture the distinctive pediatric spectrum of disease. We aim to develop specialized pediatric phecodes (Peds-Phecodes) to enable efficient, large-scale phenotypic analyses of pediatric patients.

MATERIALS AND METHODS

We adopted a hybrid data- and knowledge-driven approach leveraging electronic health records (EHRs) and genetic data from Vanderbilt University Medical Center to modify the most recent version of phecodes to better capture pediatric phenotypes. First, we compared the prevalence of patient diagnoses in pediatric and adult populations to identify disease phenotypes differentially affecting children and adults. We then used clinical domain knowledge to remove phecodes representing phenotypes unlikely to affect pediatric patients and create new phecodes for phenotypes relevant to the pediatric population. We further compared phenome-wide association study (PheWAS) outcomes replicating known pediatric genotype-phenotype associations between Peds-Phecodes and phecodes.

RESULTS

The Peds-Phecodes aggregate 15 533 ICD-9-CM codes and 82 949 ICD-10-CM codes into 2051 distinct phecodes. Peds-Phecodes replicated more known pediatric genotype-phenotype associations than phecodes (248 vs 192 out of 687 SNPs, P < .001).

DISCUSSION

We introduce Peds-Phecodes, a high-throughput EHR phenotyping tool tailored for use in pediatric populations. We successfully validated the Peds-Phecodes using genetic replication studies. Our findings also reveal the potential use of Peds-Phecodes in detecting novel genotype-phenotype associations for pediatric conditions. We expect that Peds-Phecodes will facilitate large-scale phenomic and genomic analyses in pediatric populations.

CONCLUSION

Peds-Phecodes capture higher-quality pediatric phenotypes and deliver superior PheWAS outcomes compared to phecodes.

Plant science in the age of simulation intelligence

Historically, plant and crop sciences have been quantitative fields that intensively use measurements and modeling. Traditionally, researchers choose between two dominant modeling approaches: mechanistic plant growth models or data-driven, statistical methodologies. At the intersection of both paradigms, a novel approach referred to as "simulation intelligence", has emerged as a powerful tool for comprehending and controlling complex systems, including plants and crops. This work explores the transformative potential for the plant science community of the nine simulation intelligence motifs, from understanding molecular plant processes to optimizing greenhouse control. Many of these concepts, such as surrogate models and agent-based modeling, have gained prominence in plant and crop sciences. In contrast, some motifs, such as open-ended optimization or program synthesis, still need to be explored further. The motifs of simulation intelligence can potentially revolutionize breeding and precision farming towards more sustainable food production.

Image-based classification of wheat spikes by glume pubescence using convolutional neural networks

Introduction

Pubescence is an important phenotypic trait observed in both vegetative and generative plant organs. Pubescent plants demonstrate increased resistance to various environmental stresses such as drought, low temperatures, and pests. It serves as a significant morphological marker and aids in selecting stress-resistant cultivars, particularly in wheat. In wheat, pubescence is visible on leaves, leaf sheath, glumes and nodes. Regarding glumes, the presence of pubescence plays a pivotal role in its classification. It supplements other spike characteristics, aiding in distinguishing between different varieties within the wheat species. The determination of pubescence typically involves visual analysis by an expert. However, methods without the use of binocular loupe tend to be subjective, while employing additional equipment is labor-intensive. This paper proposes an integrated approach to determine glume pubescence presence in spike images captured under laboratory conditions using a digital camera and convolutional neural networks.

Methods

Initially, image segmentation is conducted to extract the contour of the spike body, followed by cropping of the spike images to an equal size. These images are then classified based on glume pubescence (pubescent/glabrous) using various convolutional neural network architectures (Resnet-18, EfficientNet-B0, and EfficientNet-B1). The networks were trained and tested on a dataset comprising 9,719 spike images.

Results

For segmentation, the U-Net model with EfficientNet-B1 encoder was chosen, achieving the segmentation accuracy IoU = 0.947 for the spike body and 0.777 for awns. The classification model for glume pubescence with the highest performance utilized the EfficientNet-B1 architecture. On the test sample, the model exhibited prediction accuracy parameters of F1 = 0.85 and AUC = 0.96, while on the holdout sample it showed F1 = 0.84 and AUC = 0.89. Additionally, the study investigated the relationship between image scale, artificial distortions, and model prediction performance, revealing that higher magnification and smaller distortions yielded a more accurate prediction of glume pubescence.

Phenotypic characterization of drought responses in red clover (Trifolium pratense L.)

Introduction

Red clover (Trifolium pratense) is a protein-rich, short-lived perennial forage crop that can achieve high yields, but suffers increasingly from drought in different cultivation areas. Breeding for increased adaptation to drought is becoming essential, but at this stage it is unclear which traits breeders should target to phenotype responses to drought that allow them to identify the most promising red clover genotypes. In this study, we assessed how prolonged periods of drought affected plant growth in field conditions, and which traits could be used to distinguish better adapted plant material.

Methods

A diverse panel of 395 red clover accessions was evaluated during two growing seasons. We simulated 6-to-8-week drought periods during two consecutive summers, using mobile rain-out shelters, while an irrigated control field was established in an adjacent parcel. Plant growth was monitored throughout both growing seasons using multiple flights with a drone equipped with RGB and thermal sensors. At various observation moments throughout both growing seasons, we measured canopy cover (CC) and canopy height (CH). The crop water stress index (CWSI) was determined at two moments, during or shortly after the drought event.

Results

Manual and UAV-derived measurements for CH were well correlated, indicating that UAV-derived measurements can be reliably used in red clover. In both years, CC, CH and CWSI were affected by drought, with measurable growth reductions by the end of the drought periods, and during the recovery phase. We found that the end of the drought treatment and the recovery phase of approximately 20 days after drought were suitable periods to phenotype drought responses and to distinguish among genotypes.

Discussion

Multifactorial analysis of accession responses revealed interactions of the maturity type with drought responses, which suggests the presence of two independent strategies in red clover: 'drought tolerance' and 'drought recovery'. We further found that a large proportion of the accessions able to perform well under well-watered conditions were also the ones that were less affected by drought. The results of this investigation are interpreted in view of the development of breeding for adaptation to drought in red clover.

Integrated web portal for non-destructive salt sensitivity detection of Camelina sativa seeds using fluorescent and visible light images coupled with machine learning algorithms

Climate change has created unprecedented stresses in the agricultural sector, driving the necessity of adapting agricultural practices and developing novel solutions to the food crisis. Camelina sativa (Camelina) is a recently emerging oilseed crop with high nutrient-density and economic potential. Camelina seeds are rich in essential fatty acids and contain potent antioxidants required to maintain a healthy diet. Camelina seeds are equally amenable to economic applications such as jet fuel, biodiesel and high-value industrial lubricants due to their favorable proportions of unsaturated fatty acids. High soil salinity is one of the major abiotic stresses threatening the yield and usability of such crops. A promising mitigation strategy is automated, non-destructive, image-based phenotyping to assess seed quality in the food manufacturing process. In this study, we evaluate the effectiveness of image-based phenotyping on fluorescent and visible light images to quantify and qualify Camelina seeds. We developed a user-friendly web portal called SeedML that can uncover key morpho-colorimetric features to accurately identify Camelina seeds coming from plants grown in high salt conditions using a phenomics platform equipped with fluorescent and visible light cameras. This portal may be used to enhance quality control, identify stress markers and observe yield trends relevant to the agricultural sector in a high throughput manner. Findings of this work may positively contribute to similar research in the context of the climate crisis, while supporting the implementation of new quality controls tools in the agri-food domain.

Field phenotyping of ten wheat cultivars under elevated CO2 shows seasonal differences in chlorophyll fluorescence, plant height and vegetation indices

In the context of climate change and global sustainable development goals, future wheat cultivation has to master various challenges at a time, including the rising atmospheric carbon dioxide concentration ([CO2]). To investigate growth and photosynthesis dynamics under the effects of ambient (~434 ppm) and elevated [CO2] (~622 ppm), a Free-Air CO2 Enrichment (FACE) facility was combined with an automated phenotyping platform and an array of sensors. Ten modern winter wheat cultivars (Triticum aestivum L.) were monitored over a vegetation period using a Light-induced Fluorescence Transient (LIFT) sensor, ground-based RGB cameras and a UAV equipped with an RGB and multispectral camera. The LIFT sensor enabled a fast quantification of the photosynthetic performance by measuring the operating efficiency of Photosystem II (Fq'/Fm') and the kinetics of electron transport, i.e. the reoxidation rates Fr1' and Fr2'. Our results suggest that elevated [CO2] significantly increased Fq'/Fm' and plant height during the vegetative growth phase. As the plants transitioned to the senescence phase, a pronounced decline in Fq'/Fm' was observed under elevated [CO2]. This was also reflected in the reoxidation rates Fr1' and Fr2'. A large majority of the cultivars showed a decrease in the harvest index, suggesting a different resource allocation and indicating a potential plateau in yield progression under e[CO2]. Our results indicate that the rise in atmospheric [CO2] has significant effects on the cultivation of winter wheat with strong manifestation during early and late growth.

"Chamber #8" - a holistic approach of high-throughput non-destructive assessment of plant roots

Introduction

In the past years, it has been observed that the breeding of plants has become more challenging, as the visible difference in phenotypic data is much smaller than decades ago. With the ongoing climate change, it is necessary to breed crops that can cope with shifting climatic conditions. To select good breeding candidates for the future, phenotypic experiments can be conducted under climate-controlled conditions. Above-ground traits can be assessed with different optical sensors, but for the root growth, access to non-destructively measured traits is much more challenging. Even though MRI or CT imaging techniques have been established in the past years, they rely on an adequate infrastructure for the automatic handling of the pots as well as the controlled climate.

Methods

To address both challenges simultaneously, the non-destructive imaging of plant roots combined with a highly automated and standardized mid-throughput approach, we developed a workflow and an integrated scanning facility to study root growth. Our "chamber #8" contains a climate chamber, a material flow control, an irrigation system, an X-ray system, a database for automatic data collection, and post-processing. The goals of this approach are to reduce the human interaction with the various components of the facility to a minimum on one hand, and to automate and standardize the complete process from plant care via measurements to root trait calculation on the other. The user receives standardized phenotypic traits and properties that were collected objectively.

Results

The proposed holistic approach allows us to study root growth of plants in a field-like substrate non-destructively over a defined period and to calculate phenotypic traits of root architecture. For different crops, genotypic differences can be observed in response to climatic conditions which have already been applied to a wide variety of root structures, such as potatoes, cassava, or corn.

Discussion

It enables breeders and scientists non-destructive access to root traits. Additionally, due to the non-destructive nature of X-ray computed tomography, the analysis of time series for root growing experiments is possible and enables the observation of kinetic traits. Furthermore, using this automation scheme for simultaneously controlled plant breeding and non-destructive testing reduces the involvement of human resources.

Discovery and validation of genes driving drug-intake and related behavioral traits in mice

Substance use disorders are heritable disorders characterized by compulsive drug use, the biological mechanisms for which remain largely unknown. Genetic correlations reveal that predisposing drug-naïve phenotypes, including anxiety, depression, novelty preference and sensation seeking, are predictive of drug-use phenotypes, thereby implicating shared genetic mechanisms. High-throughput behavioral screening in knockout (KO) mice allows efficient discovery of the function of genes. We used this strategy in two rounds of candidate prioritization in which we identified 33 drug-use candidate genes based upon predisposing drug-naïve phenotypes and ultimately validated the perturbation of 22 genes as causal drivers of substance intake. We selected 19/221 KO strains (8.5%) that had a difference from control on at least one drug-naïve predictive behavioral phenotype and determined that 15/19 (~80%) affected the consumption or preference for alcohol, methamphetamine or both. No mutant exhibited a difference in nicotine consumption or preference which was possibly confounded with saccharin. In the second round of prioritization, we employed a multivariate approach to identify outliers and performed validation using methamphetamine two-bottle choice and ethanol drinking-in-the-dark protocols. We identified 15/401 KO strains (3.7%, which included one gene from the first cohort) that differed most from controls for the predisposing phenotypes. 8 of 15 gene deletions (53%) affected intake or preference for alcohol, methamphetamine or both. Using multivariate and bioinformatic analyses, we observed multiple relations between predisposing behaviors and drug intake, revealing many distinct biobehavioral processes underlying these relationships. The set of mouse models identified in this study can be used to characterize these addiction-related processes further.

Root Walker: an automated pipeline for large scale quantification of early root growth responses at high spatial and temporal resolution

Plants are sessile organisms that constantly adapt to their changing environment. The root is exposed to numerous environmental signals ranging from nutrients and water to microbial molecular patterns. These signals can trigger distinct responses including the rapid increase or decrease of root growth. Consequently, using root growth as a readout for signal perception can help decipher which external cues are perceived by roots, and how these signals are integrated. To date, studies measuring root growth responses using large numbers of roots have been limited by a lack of high-throughput image acquisition, poor scalability of analytical methods, or low spatiotemporal resolution. Here, we developed the Root Walker pipeline, which uses automated microscopes to acquire time-series images of many roots exposed to controlled treatments with high spatiotemporal resolution, in conjunction with fast and automated image analysis software. We demonstrate the power of Root Walker by quantifying root growth rate responses at different time and throughput scales upon treatment with natural auxin and two mitogen-associated protein kinase cascade inhibitors. We find a concentration-dependent root growth response to auxin and reveal the specificity of one MAPK inhibitor. We further demonstrate the ability of Root Walker to conduct genetic screens by performing a genome-wide association study on 260 accessions in under 2 weeks, revealing known and unknown root growth regulators. Root Walker promises to be a useful toolkit for the plant science community, allowing large-scale screening of root growth dynamics for a variety of purposes, including genetic screens for root sensing and root growth response mechanisms.

A Hydroponic-Based Bioassay to Facilitate Plasmodiophora brassicae Phenotyping

Clubroot, caused by the obligate parasite Plasmodiophora brassicae, is one of the most devastating diseases affecting the canola/oilseed rape (Brassica napus) industry worldwide. Currently, the planting of clubroot-resistant (CR) cultivars is the most effective strategy used to restrict the spread and the economic losses linked to the disease. However, virulent P. brassicae isolates have been able to infect many of the currently available CR cultivars, and the options to manage the disease are becoming limited. Another challenge has been achieving consistency in evaluating host reactions to P. brassicae infection, with most bioassays conducted in soil and/or potting medium, which requires significant space and can be labor intensive. Visual scoring of clubroot symptom development can also be influenced by user bias. Here, we have developed a hydroponic bioassay using well-characterized P. brassicae single-spore isolates representative of clubroot virulence in Canada, as well as field isolates from three Canadian provinces in combination with canola inbred homozygous lines carrying resistance genetics representative of CR cultivars available to growers in Canada. To improve the efficiency and consistency of disease assessment, symptom severity scores were compared with clubroot evaluations based on the scanned root area. According to the results, this bioassay offers a reliable, less expensive, and reproducible option to evaluate P. brassicae virulence, as well as to identify which canola resistance profile(s) may be effective against particular isolates. This bioassay will contribute to the breeding of new CR canola cultivars and the identification of virulence genes in P. brassicae that could trigger resistance and that have been very elusive to this day.[Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Nickel Challenge In Vitro Affects CD38 and HLA-DR Expression in T Cell Subpopulations from the Blood of Patients with Nickel Allergy

Nickel allergy is a major health problem and shows clinical manifestation of contact eczema. The response of specific lymphocyte subpopulations in sensitized patients after new challenge to nickel has until now not been studied in detail. To evaluate if nickel-based elicitation reaction could be objectively identified by multi-parametric flow cytometry, immunophenotyping of specific T cells was applied. White blood cells from 7 patients (4 positive in patch test, 3 negative) were challenged by nickel and in vitro short-term culture. Standardized antibody-dye combinations, specific for T helper(h)1, Th17 and cytotoxic T cell activation, were selected according to the recommendations of Stanford Human Immune Monitoring Center. In cytotoxic CD8+CCR7+CD45RA+ T cells from patients suffering from nickel allergy, CD38 and HLA-DR were elevated comparing to healthy donors. After challenge to nickel in vitro both markers decreased in CD8+CCR7+CD45RA+ T cells but found up-regulated in CD4+CCR7+CD45RA+CCR6-CXCR3+Th1 cells. Intracellular expression of T-bet and RORγt further indicated Th1 and Th17 cells. Finally, CD4+CD25+CCR4- T cells increased after challenge with nickel in PBMCs of patients with nickel allergy. Flow cytometry based quantification of T cell markers might be used as a specific and reliable method to detect chemical induced skin sensitization and confirm diagnostic patch testing in the clinics.

Scalable and interpretable alternative to chart review for phenotype evaluation using standardized structured data from electronic health records

OBJECTIVES

Chart review as the current gold standard for phenotype evaluation cannot support observational research on electronic health records and claims data sources at scale. We aimed to evaluate the ability of structured data to support efficient and interpretable phenotype evaluation as an alternative to chart review.

MATERIALS AND METHODS

We developed Knowledge-Enhanced Electronic Profile Review (KEEPER) as a phenotype evaluation tool that extracts patient's structured data elements relevant to a phenotype and presents them in a standardized fashion following clinical reasoning principles. We evaluated its performance (interrater agreement, intermethod agreement, accuracy, and review time) compared to manual chart review for 4 conditions using randomized 2-period, 2-sequence crossover design.

RESULTS

Case ascertainment with KEEPER was twice as fast compared to manual chart review. 88.1% of the patients were classified concordantly using charts and KEEPER, but agreement varied depending on the condition. Missing data and differences in interpretation accounted for most of the discrepancies. Pairs of clinicians agreed in case ascertainment in 91.2% of the cases when using KEEPER compared to 76.3% when using charts. Patient classification aligned with the gold standard in 88.1% and 86.9% of the cases respectively.

CONCLUSION

Structured data can be used for efficient and interpretable phenotype evaluation if they are limited to relevant subset and organized according to the clinical reasoning principles. A system that implements these principles can achieve noninferior performance compared to chart review at a fraction of time.

A Least Absolute Shrinkage and Selection Operator-Derived Predictive Model for Postoperative Respiratory Failure in a Heterogeneous Adult Elective Surgery Patient Population

BACKGROUND

Postoperative respiratory failure (PRF) is associated with increased hospital charges and worse patient outcomes. Reliable prediction models can help to guide postoperative planning to optimize care, to guide resource allocation, and to foster shared decision-making with patients.

RESEARCH QUESTION

Can a predictive model be developed to accurately identify patients at high risk of PRF?

STUDY DESIGN AND METHODS

In this single-site proof-of-concept study, we used structured query language to extract, transform, and load electronic health record data from 23,999 consecutive adult patients admitted for elective surgery (2014-2021). Our primary outcome was PRF, defined as mechanical ventilation after surgery of > 48 h. Predictors of interest included demographics, comorbidities, and intraoperative factors. We used logistic regression to build a predictive model and the least absolute shrinkage and selection operator procedure to select variables and to estimate model coefficients. We evaluated model performance using optimism-corrected area under the receiver operating curve and area under the precision-recall curve and calculated sensitivity, specificity, positive and negative predictive values, and Brier scores.

RESULTS

Two hundred twenty-five patients (0.94%) demonstrated PRF. The 18-variable predictive model included: operations on the cardiovascular, nervous, digestive, urinary, or musculoskeletal system; surgical specialty orthopedic (nonspine); Medicare or Medicaid (as the primary payer); race unknown; American Society of Anesthesiologists class ≥ III; BMI of 30 to 34.9 kg/m2; anesthesia duration (per hour); net fluid at end of the operation (per liter); median intraoperative FIO2, end title CO2, heart rate, and tidal volume; and intraoperative vasopressor medications. The optimism-corrected area under the receiver operating curve was 0.835 (95% CI,0.808-0.862) and the area under the precision-recall curve was 0.156 (95% CI, 0.105-0.203).

INTERPRETATION

This single-center proof-of-concept study demonstrated that a structured query language extract, transform, and load process, based on readily available patient and intraoperative variables, can be used to develop a prediction model for PRF. This PRF prediction model is scalable for multicenter research. Clinical applications include decision support to guide postoperative level of care admission and treatment decisions.

Evobrutinib pathway to its major metabolite M463-2 and insights from a biotransformation and DDI perspective

Evobrutinib is a highly selective, covalent, central nervous system-penetrant Bruton's tyrosine kinase (BTK) inhibitor, currently in Phase III trials for the treatment of relapsing multiple sclerosis. One major circulating metabolite of evobrutinib has been previously identified as the racemic dihydro-diol M463-2 (MSC2430422) in a Phase I human mass balance study.Phenotyping experiments were conducted to confirm the metabolic pathway of evobrutinib to M463-2. Ratio of the enantiomers was determined by enantioselective liquid chromatography with tandem mass spectrometry analysis of plasma samples from humans and preclinical species. Drug-drug interaction (DDI) characterisation, evaluation of pharmacological activity on BTK, and off-target screening experiments followed assessing safety of the metabolite.The biotransformation of evobrutinib to M463-2 was determined to be a two-step process with a CYP-mediated oxidation acting to form an epoxide intermediate, which was further hydrolysed by soluble and mitochondrial epoxide hydrolase. Only the (S)-enantiomer was determined to be a major metabolite, the (R)-enantiomer was minor. In vitro studies demonstrated the (S)-enantiomer lacked clinically relevant pharmacological activity, off-target effects and DDIs.The biotransformation of evobrutinib to its major metabolite has been elucidated, with the major (S)-enantiomer being shown to pose no on/off target or DDI risks.

Insomnia nosology: a systematic review and critical appraisal of historical diagnostic categories and current phenotypes

Insomnia nosology has significantly evolved since the Diagnostic and Statistical Manual (DSM)-III-R first distinguished between 'primary' and 'secondary' insomnia. Prior International Classification of Sleep Disorders (ICSD) nosology 'split' diagnostic phenotypes to address insomnia's heterogeneity and the DSM nosology 'lumped' them into primary insomnia, while both systems assumed causality for insomnia secondary to health conditions. In this systematic review, we discuss the historical phenotypes in prior insomnia nosology, present findings for currently proposed insomnia phenotypes based on more robust approaches, and critically appraise the most relevant ones. Electronic databases PsychINFO, PubMED, Web of Science, and references of eligible articles, were accessed to find diagnostic manuals, literature on insomnia phenotypes, including systematic reviews or meta-analysis, and assessments of the reliability or validity of insomnia diagnoses, identifying 184 articles. The data show that previous insomnia diagnoses lacked reliability and validity, leading current DSM-5-TR and ICSD-3 nosology to 'lump' phenotypes into a single diagnosis comorbid with health conditions. However, at least two new, robust insomnia phenotyping approaches were identified. One approach is multidimensional-multimethod and provides evidence for self-reported insomnia with objective short versus normal sleep duration linked to clinically relevant outcomes, while the other is multidimensional and provides evidence for two to five clusters (phenotypes) based on self-reported trait, state, and/or life-history data. Some approaches still need replication to better support whether their findings identify true phenotypes or simply different patterns of symptomatology. Regardless, these phenotyping efforts aim at improving insomnia nosology both as a classification system and as a mechanism to guide treatment.

Development and application of pharmacological statin-associated muscle symptoms phenotyping algorithms using structured and unstructured electronic health records data

Importance

Statins are widely prescribed cholesterol-lowering medications in the United States, but their clinical benefits can be diminished by statin-associated muscle symptoms (SAMS), leading to discontinuation.

Objectives

In this study, we aimed to develop and validate a pharmacological SAMS clinical phenotyping algorithm using electronic health records (EHRs) data from Minnesota Fairview.

Materials and Methods

We retrieved structured and unstructured EHR data of statin users and manually ascertained a gold standard set of SAMS cases and controls using the published SAMS-Clinical Index tool from clinical notes in 200 patients. We developed machine learning algorithms and rule-based algorithms that incorporated various criteria, including ICD codes, statin allergy, creatine kinase elevation, and keyword mentions in clinical notes. We applied the best-performing algorithm to the statin cohort to identify SAMS.

Results

We identified 16 889 patients who started statins in the Fairview EHR system from 2010 to 2020. The combined rule-based (CRB) algorithm, which utilized both clinical notes and structured data criteria, achieved similar performance compared to machine learning algorithms with a precision of 0.85, recall of 0.71, and F1 score of 0.77 against the gold standard set. Applying the CRB algorithm to the statin cohort, we identified the pharmacological SAMS prevalence to be 1.9% and selective risk factors which included female gender, coronary artery disease, hypothyroidism, and use of immunosuppressants or fibrates.

Discussion and Conclusion

Our study developed and validated a simple pharmacological SAMS phenotyping algorithm that can be used to create SAMS case/control cohort to enable further analysis which can lead to the development of a SAMS risk prediction model.

Sequential multi-omics analysis identifies clinical phenotypes and predictive biomarkers for long COVID

The post-acute sequelae of COVID-19 (PASC), also known as long COVID, is often associated with debilitating symptoms and adverse multisystem consequences. We obtain plasma samples from 117 individuals during and 6 months following their acute phase of infection to comprehensively profile and assess changes in cytokines, proteome, and metabolome. Network analysis reveals sustained inflammatory response, platelet degranulation, and cellular activation during convalescence accompanied by dysregulation in arginine biosynthesis, methionine metabolism, taurine metabolism, and tricarboxylic acid (TCA) cycle processes. Furthermore, we develop a prognostic model composed of 20 molecules involved in regulating T cell exhaustion and energy metabolism that can reliably predict adverse clinical outcomes following discharge from acute infection with 83% accuracy and an area under the curve (AUC) of 0.96. Our study reveals pertinent biological processes during convalescence that differ from acute infection, and it supports the development of specific therapies and biomarkers for patients suffering from long COVID.

Comparing CNNs and PLSr for estimating wheat organs biophysical variables using proximal sensing

Estimation of biophysical vegetation variables is of interest for diverse applications, such as monitoring of crop growth and health or yield prediction. However, remote estimation of these variables remains challenging due to the inherent complexity of plant architecture, biology and surrounding environment, and the need for features engineering. Recent advancements in deep learning, particularly convolutional neural networks (CNN), offer promising solutions to address this challenge. Unfortunately, the limited availability of labeled data has hindered the exploration of CNNs for regression tasks, especially in the frame of crop phenotyping. In this study, the effectiveness of various CNN models in predicting wheat dry matter, nitrogen uptake, and nitrogen concentration from RGB and multispectral images taken from tillering to maturity was examined. To overcome the scarcity of labeled data, a training pipeline was devised. This pipeline involves transfer learning, pseudo-labeling of unlabeled data and temporal relationship correction. The results demonstrated that CNN models significantly benefit from the pseudolabeling method, while the machine learning approach employing a PLSr did not show comparable performance. Among the models evaluated, EfficientNetB4 achieved the highest accuracy for predicting above-ground biomass, with an R² value of 0.92. In contrast, Resnet50 demonstrated superior performance in predicting LAI, nitrogen uptake, and nitrogen concentration, with R² values of 0.82, 0.73, and 0.80, respectively. Moreover, the study explored multi-output models to predict the distribution of dry matter and nitrogen uptake between stem, inferior leaves, flag leaf, and ear. The findings indicate that CNNs hold promise as accessible and promising tools for phenotyping quantitative biophysical variables of crops. However, further research is required to harness their full potential.

Phenotypes, Etiotypes, and Endotypes of Exacerbations of Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease is a major health problem with a high prevalence, a rising incidence, and substantial morbidity and mortality. Its course is punctuated by acute episodes of increased respiratory symptoms, termed exacerbations of chronic obstructive pulmonary disease (ECOPD). ECOPD are important events in the natural history of the disease, as they are associated with lung function decline and prolonged negative effects on quality of life. The present-day therapy for ECOPD with short courses of antibiotics and steroids and escalation of bronchodilators has resulted in only modest improvements in outcomes. Recent data indicate that ECOPD are heterogeneous, raising the need to identify distinct etioendophenotypes, incorporating traits of the acute event and of patients who experience recurrent events, to develop novel and targeted therapies. These characterizations can provide a complete clinical picture, the severity of which will dictate acute pharmacological treatment, and may also indicate whether a change in maintenance therapy is needed to reduce the risk of future exacerbations. In this review we discuss the latest knowledge of ECOPD types on the basis of clinical presentation, etiology, natural history, frequency, severity, and biomarkers in an attempt to characterize these events.