Precise Phenotyping Research Articles

Machine vision-based detection of key traits in shiitake mushroom caps

This study puts forward a machine vision-based prediction method to solve the problem regarding the measurement of traits in shiitake mushroom caps during the shiitake mushroom breeding process. It enables precise phenotyping through accurate image acquisition and analysis. In practical applications, this method improves the breeding process by rapidly and non-invasively assessing key traits such as the size and color of shiitake mushroom caps, which helps in efficiently screening strains and reducing human errors. Firstly, an edge detection model was established. This model is called KL-Dexined. It achieved an per-image best threshold (OIS) rate of 93.5%. Also, it reached an Optimal Dynamic Stabilization (ODS) rate of 96.3%. Moreover, its Average Precision (AP) was 97.1%. Secondly, the edge information detected by KL-Dexined was mapped onto the original image of shiitake mushroom caps, and using the OpenCV model,11 phenotypic key features including shiitake mushroom caps area, perimeter, and external rectangular length were obtained. Experimental results demonstrated that the R² between predicted values and true values was 0.97 with an RMSE as low as 0.049. After conducting correlation analysis between phenotypic features and shiitake mushroom caps weight, four most correlated phenotypic features were identified: Area, Perimeter, External rectangular width, and Long axis; they were divided into four groups based on their correlation rankings. Finally,M3 group using GWO_SVM algorithm achieved optimal performance among six mainstream machine learning models tested with an R²value of 0.97 and RMSE only at 0.038 when comparing predicted values with true values. Hence, this study provided guidance for predicting key traits in shiitake mushroom caps.

Prenatal metal(loid) exposure and preterm birth: a systematic review of the epidemiologic evidence.

Preterm birth (PTB) is a common pregnancy complication associated with significant neonatal morbidity. Prenatal exposure to environmental chemicals, including toxic and/or essential metal(loid)s, may contribute to PTB risk. We aimed to summarize the epidemiologic evidence of the associations amonglevels ofarsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), manganese (Mn), lead (Pb), and zinc (Zn) assessed during the prenatal periodandPTB or gestational age at delivery; to assess the quality of the literature and strength of evidence for an effect for each metal; and to provide recommendations for future research. We adapted the Navigation Guide methodology and followed PRISMA guidelines. We searched the MEDLINE/PubMed database for epidemiologic studies from 1995 to 2023. We used a customized risk of bias protocol and evaluated the sufficiency of evidence for an effect of each metal(loid) on PTB risk. A total of 1206 studies were identified and screened. Of these, 139 were assessed for eligibility by reading the full-text, and 92 studies were ultimately included (arsenic: 40, cadmium: 30, chromium: 11, copper: 21, mercury: 27, manganese: 17, lead: 41, zinc: 18, metal(loid) mixtures: 12). We found sufficient evidence that lead increases the risk of PTB and, while the evidence was limited, suggestive evidence that cadmium and chromium increase the risk of PTB. The evidence was deemed inadequate to determine an effect for the other metal(loid)s. Future research would benefit from more precise PTB clinical phenotyping, measuring exposure early and longitudinally throughout pregnancy, using an appropriate media for metal(loid)s under study, and evaluating metal mixtures. Given the strength of evidence linking lead exposure and PTB, active and comprehensive prenatal screening for lead exposure among pregnant individuals is warranted. By summarizing 92 epidemiologic studies that investigated the associations between metal exposure and preterm birth using the rigorous Navigation Guide methodology, our review provides compelling evidence for a strong link between prenatal lead exposure and preterm birth. Additionally, it suggests potential associations between cadmium and chromium exposure and preterm birth. Given the robust nature of this evidence, there is an urgent need for prenatal screening for lead exposure during pregnancy, along with targeted interventions to reduce exposure. These actions are critical for advancing maternal and child health.

Precise phenotyping method using image data for carcass marbling score in Hanwoo cattle.

With the development of the Korean economy, demand for high-quality beef, specifically Hanwoo beef, is escalating, with marbling traits-measured by the widely used marbling score-being a key contributor to meat palatability. The differences between the high-quality and the lower-quality meat, according to the satisfaction of the customers, are not the result from only the degree of marbling but also from the delicacy of the marbling flecks distribution. Using the computer marbling analysis technique, an index for quantifying marbling fineness of 256 sirloin cuts at 12th- 13th thoracic vertebra named F7 index was developed in this study. F7 index is defined as the standard deviation of the ratios of marbling particles area to the tile area, was developed in this study. At the optimal step size of 70 tiles per axis, F7 index discriminated dramatically the finely marbled and coarsely marbled sirloin with Beef Marbling Score from 6 to 9, with P value = 1.340 × 10-27 < 0.05 of. Although the efficiency of computer image analysis procedure with the F7 index is still being optimized, the F7 index shows great potential to enhance the accuracy of Hanwoo beef quality grading alongside marbling score and support the development of improved breeding strategies for the Hanwoo cattle population in Korea.

Study on genetics of yield component traits under salt stress in two rice crosses raised using honeycomb selection design

Abstract Yield components related to grain yield were severely affected by salinity. For the agronomically important traits, precise phenotyping of crop plants is essential under stress where soil heterogeneity is aggravated for genetic analysis and developing improved crop varieties. A set of two F2 populations obtained from ADT(R)45 × FL478 (A × F) and ADT(R)45 × Nona Bokra (A × N) crosses along with check variety CSR10 were raised in Honeycomb Selection Design (HSD) to study the genetics of yield component traits under salt stress. The study found that the trait, panicle weight in A × F exhibited normal distribution while the other traits exhibited non-normal distribution in both the crosses. Negative skewness was observed for panicle length in both the crosses and days to flowering in the A × N indicating duplicate (additive × additive) gene interactions. Whereas positive skewness was observed for plant height, number of tillers, productive tillers and single plant yield in both the crosses, days to flowering and spikelet sterility in A × F and panicle weight in A × N indicating complementary gene interactions. Leptokurtosis was observed for days to flowering in the A × F. While the remaining characters in both the crosses exhibited platykurtosis. Transgressive segregants in both directions (towards both the parents) were obtained for all the eight characters studied in both the crosses suggesting that all traits were governed by additive gene action. Honeycomb selection design (HSD) provides an excellent phenotyping framework for studying the genetics of any quantitative traits, particularly yield and its component traits, as well as for breeding varieties that will provide more consistent results in yield in a shorter time frame. Keywords: Skewness, kurtosis, transgressive segregation, gene action

Harnessing Speed Breeding in Controlled Environment Ecosystem for Millets Sustainability

ABSTRACTMillet breeding focuses on improving essential traits such as grain yield, head structure, tiller production, early maturity, reduced plant height, biomass, digestibility and key nutrients like vitamin B1, lysine, cysteine and methionine. Traditional breeding, especially in open environments, can take 9–17 years to release a new variety, whereas speed breeding in controlled environments shortens this to 5–9 years. This accelerated process tackles challenges like male sterility, self‐incompatibility, seed shattering, inbreeding depression and embryo abortion. Techniques such as rapid single‐seed descent enable the creation of near‐homozygous lines in 1–2 years, allowing finger millet to achieve up to five generations per year. Indoor phenotyping platforms enhance speed breeding by providing detailed, consistent monitoring of plant traits. High‐throughput systems in controlled settings like growth chambers or glasshouses allow for non‐invasive assessment of entire crop canopies, measuring traits such as leaf expansion, width, phyllochron and stomatal conductance. This precise phenotyping accelerates trait evaluation and selection, facilitating the development of superior millet varieties. Supported by advanced phenotyping and gene‐editing tools, speed breeding offers a robust solution for improving key agronomic traits, significantly reducing breeding time in controlled environments.

First Diagnostic Questionnaire for Assessing Patients' Social Functioning: Comprehensive DDX3X Syndrome Patient Profile.

Background/Objectives: DDX3X syndrome is often misdiagnosed as autism spectrum disorder (ASD, Rett Syndrome, and Dandy-Walker Syndrome). Precise phenotyping is needed with reference to neurodevelopmental diagnosis. Observation of behavior and communication in parents with DDX3X syndrome in the USA, France, and Poland; conversations with the parents of patients; and rudimentary information in evidence-based medical articles prompted us to identify differences in communication, play, and social interaction between children with ASD only, those with both ASD and DDX3X, and those with DDX3X only. Methods: As diagnostic tool for DDX3X patients, we created a questionnaire divided into four sections: medical, social, play, and communication. Results: The results showed inconsistent diagnoses in different countries where children could have been diagnosed with DDX3X. In a comparative analysis, individuals with DDX3X exhibited greater social skills than individuals with ASD. Furthermore, those with DDX3X demonstrated higher levels of social functioning compared to children with ASD. Therefore, parents of children recently diagnosed with ASD or similar conditions are encouraged to complete a survey to determine if their child is likely to have features of DDX3X syndrome. Conclusion: Identification of early behavioral markers that differentiate children with ASD and those with DDX3X could lead to the earliest opportunity for identification and intervention, and can significantly impact developmental trajectories, leading to better long-term outcomes.

Same-Slide Spatial Multi-Omics Integration Reveals Tumor Virus-Linked Spatial Reorganization of the Tumor Microenvironment.

The advent of spatial transcriptomics and spatial proteomics have enabled profound insights into tissue organization to provide systems-level understanding of diseases. Both technologies currently remain largely independent, and emerging same slide spatial multi-omics approaches are generally limited in plex, spatial resolution, and analytical approaches. We introduce IN-situ DEtailed Phenotyping To High-resolution transcriptomics (IN-DEPTH), a streamlined and resource-effective approach compatible with various spatial platforms. This iterative approach first entails single-cell spatial proteomics and rapid analysis to guide subsequent spatial transcriptomics capture on the same slide without loss in RNA signal. To enable multi-modal insights not possible with current approaches, we introduce k-bandlimited Spectral Graph Cross-Correlation (SGCC) for integrative spatial multi-omics analysis. Application of IN-DEPTH and SGCC on lymphoid tissues demonstrated precise single-cell phenotyping and cell-type specific transcriptome capture, and accurately resolved the local and global transcriptome changes associated with the cellular organization of germinal centers. We then implemented IN-DEPTH and SGCC to dissect the tumor microenvironment (TME) of Epstein-Barr Virus (EBV)-positive and EBV-negative diffuse large B-cell lymphoma (DLBCL). Our results identified a key tumor-macrophage-CD4 T-cell immunomodulatory axis differently regulated between EBV-positive and EBV-negative DLBCL, and its central role in coordinating immune dysfunction and suppression. IN-DEPTH enables scalable, resource-efficient, and comprehensive spatial multi-omics dissection of tissues to advance clinically relevant discoveries.

Research on Fine-Grained Phenotypic Analysis of Temporal Root Systems - Improved YoloV8seg Applied for Fine-Grained Analysis of In Situ Root Temporal Phenotypes.

Root systems are crucial organs for crops to absorb water and nutrients. Conducting phenotypic analysis on roots is of great importance. To date, methods for root system phenotypic analysis have predominantly focused on semantic segmentation, integrating phenotypic extraction software to achieve comprehensive root phenotype analysis. This study demonstrates the feasibility of instance segmentation tasks on in situ root system images. An improved YoloV8n-seg network tailored for detecting elongated roots is proposed, which outperforms the original YoloV8seg in all network performance metrics. Additionally, the post-processing method introduced reduces root identification errors, ensuring a one-to-one correspondence between each root system and its detection box. The experiment yields phenotypic parameters for fine-grained roots, such as fine-grained root length, diameter, and curvature. Compared to traditional parameters like total root length and average root diameter, these detailed phenotypic analyses enable more precise phenotyping and facilitate accurate artificial intervention during crop cultivation.

Mapping genes for human face shape: Exploration of univariate phenotyping strategies.

Human facial shape, while strongly heritable, involves both genetic and structural complexity, necessitating precise phenotyping for accurate assessment. Common phenotyping strategies include simplifying 3D facial features into univariate traits such as anthropometric measurements (e.g., inter-landmark distances), unsupervised dimensionality reductions (e.g., principal component analysis (PCA) and auto-encoder (AE) approaches), and assessing resemblance to particular facial gestalts (e.g., syndromic facial archetypes). This study provides a comparative assessment of these strategies in genome-wide association studies (GWASs) of 3D facial shape. Specifically, we investigated inter-landmark distances, PCA and AE-derived latent dimensions, and facial resemblance to random, extreme, and syndromic gestalts within a GWAS of 8,426 individuals of recent European ancestry. Inter-landmark distances exhibit the highest SNP-based heritability as estimated via LD score regression, followed by AE dimensions. Conversely, resemblance scores to extreme and syndromic facial gestalts display the lowest heritability, in line with expectations. Notably, the aggregation of multiple GWASs on facial resemblance to random gestalts reveals the highest number of independent genetic loci. This novel, easy-to-implement phenotyping approach holds significant promise for capturing genetically relevant morphological traits derived from complex biomedical imaging datasets, and its applications extend beyond faces. Nevertheless, these different phenotyping strategies capture different genetic influences on craniofacial shape. Thus, it remains valuable to explore these strategies individually and in combination to gain a more comprehensive understanding of the genetic factors underlying craniofacial shape and related traits.

Precision Phenotyping of Mild Behavioral Impairment and the Early Detection of Dementia

Precision Phenotyping of Mild Behavioral Impairment and the Early Detection of Dementia

Precision Phenotyping of Mild Behavioral Impairment and the Early Detection of Dementia

Precision Phenotyping of Mild Behavioral Impairment and the Early Detection of Dementia

BerryPortraits: Phenotyping Of Ripening Traits in cranberry (Vaccinium macrocarpon Ait.) with YOLOv8

BerryPortraits (Phenotyping of Ripening Traits) is open source Python-based image-analysis software that rapidly detects and segments berries and extracts morphometric data on fruit quality traits such as berry color, size, shape, and uniformity. Utilizing the YOLOv8 framework and community-developed, actively-maintained Python libraries such as OpenCV, BerryPortraits software was trained on 512 postharvest images (taken under controlled lighting conditions) of phenotypically diverse cranberry populations (Vaccinium macrocarpon Ait.) from the two largest public cranberry breeding programs in the U.S. The implementation of CIELAB, an intuitive and perceptually uniform color space, enables differentiation between berry color and berry brightness, which are confounded in classic RGB color channel measurements. Furthermore, computer vision enables precise and quantifiable color phenotyping, thus facilitating inclusion of researchers and data analysts with color vision deficiency. BerryPortraits is a phenotyping tool for researchers in plant breeding, plant genetics, horticulture, food science, plant physiology, plant pathology, and related fields. BerryPortraits has strong potential applications for other specialty crops such as blueberry, lingonberry, caneberry, grape, and more. As an open source phenotyping tool based on widely-used python libraries, BerryPortraits allows anyone to use, fork, modify, optimize, and embed this software into other tools or pipelines.

Integrating dynamic high-throughput phenotyping and genetic analysis to monitor growth variation in foxtail millet

BackgroundFoxtail millet [Setaria italica (L.) Beauv] is a C4 graminoid crop cultivated mainly in the arid and semiarid regions of China for more than 7000 years. Its grain highly nutritious and is rich in starch, protein, essential vitamins such as carotenoids, folate, and minerals. To expand the utilisation of foxtail millet, efficient and precise methods for dynamic phenotyping of its growth stages are needed. Traditional foxtail millet monitoring methods have high labour costs and are inefficient and inaccurate, impeding the precise evaluation of foxtail millet genotypic variation.ResultsThis study introduces a high-throughput imaging system (HIS) with advanced image processing techniques to enhance monitoring efficiency and data quality. The HIS can accurately extract a range of key growth feature parameters, such as plant height (PH), convex hull area (CHA), side projected area (SPA) and colour distribution, from foxtail millet images. Compared with traditional manual measurements, this HIS improved data quality and phenotyping of the key foxtail millet growth traits. High-throughput phenotyping combined with a genome-wide association study (GWAS) revealed genetic loci associated with dynamic growth traits, particularly plant height (PH), in foxtail millet. The loci were linked to genes involved in the gibberellic acid (GA) synthesis pathway related to PH.ConclusionThe HIS developed in this study enables the efficient and dynamic monitoring of foxtail millet phenotypic traits. It significantly improves the quality of data obtained for phenotyping key growth traits. The integration of high-throughput phenotyping with GWAS provides new insights into the genetic underpinnings of dynamic growth traits, particularly plant height, by identifying associated genetic loci in the GA synthesis pathway. This methodological advancement opens new avenues for the precise phenotyping and exploration of genetic resources in foxtail millet, potentially enhancing its utilisation.

Response to oxalic acid: an important supplement screening against stem rot resistance in groundnut (Arachis hypogaea L.)

BackgroundStem rot, caused by the soil-borne pathogen Sclerotium rolfsii, pose a serious challenge in the groundnut (Arachis hypogaea L) cultivation. Although this disease is widespread globally but had most adverse impact in groundnut growing regions of United States, India, and Australia. The pathogen primarily targets the crown region of the plant, resulting in systemic collapse and potentially leading to yield losses up to 80%. Effective genetic control measures are essential to mitigate the impact of this disease on groundnut production. Realizing the time and resource-consuming complex field-based phenotyping, the availability of easy and repeatable phenotyping methods may fasten the process of donor and gene discovery efforts.ResultsMulti-season phenotyping was performed for stem rot on 184 minicore germplasm accessions, including checks, under two conditions: sick field screening and response to oxalic acid assay. This study demonstrated medium to high heritability (52–63% broad-sense heritability) and significant environmental influence (36%). The response to the oxalic acid assay showed a high proportion of similarity (approximately 80%) with the percent mortality observed in the sick field indicating an easy way of performing precise phenotyping. Notably, seven genotypes—ICG163, ICG721, ICG10479, ICG875, ICG11457, ICG111, and ICG2857—exhibited stable resistance, with less than 30% mortality against stem rot disease. Among these, ICG163, ICG875, and ICG111 displayed low mortality and consistent stability across multiple seasons in both the sick field and controlled conditions of the oxalic acid assay.ConclusionsThe oxalic acid assay developed in this study effectively complements field phenotyping, as a reliable method for assessing stem rot resistance. Seven resistant genotypes identified through this assay can be utilized for the introgression of stem rot resistance into elite genotypes. Given the significant influence of the environment on stem rot resistance, it is essential to implement multi-season phenotyping to obtain precise results. Furthermore, the response to oxalic acid serves as a valuable supplement to traditional field phenotyping, since maintaining uniform disease pressure during field screenings is often challenging.

Precision phenotyping for curating research cohorts of patients with unexplained post-acute sequelae of COVID-19.

Scalable identification of patients with post-acute sequelae of COVID-19 (PASC) is challenging due to a lack of reproducible precision phenotyping algorithms, which has led to suboptimal accuracy, demographic biases, and underestimation of the PASC. In a retrospective case-control study, we developed a precision phenotyping algorithm for identifying cohorts of patients with PASC. We used longitudinal electronic health records data from over 295,000 patients from 14 hospitals and 20 community health centers in Massachusetts. The algorithm employs an attention mechanism to simultaneously exclude sequelae that prior conditions can explain and include infection-associated chronic conditions. We performed independent chart reviews to tune and validate the algorithm. The PASC phenotyping algorithm improves precision and prevalence estimation and reduces bias in identifying PASC cohorts compared to the ICD-10-CM code U09.9. The algorithm identified a cohort of over 24,000 patients with 79.9% precision. Our estimated prevalence of PASC was 22.8%, which is close to the national estimates for the region. We also provide in-depth analyses, encompassing identified lingering effects by organ, comorbidity profiles, and temporal differences in the risk of PASC. PASC precision phenotyping boasts superior precision and prevalence estimation while exhibiting less bias in identifying patients with PASC. The cohort derived from this algorithm will serve as a springboard for delving into the genetic, metabolomic, and clinical intricacies of PASC, surmounting the constraints of prior PASC cohort studies. This research was funded by the US National Institute of Allergy and Infectious Diseases (NIAID).

Precision phenotyping from routine laboratory parameters for out of hospital survival prediction in an all comers prospective PCI registry

Out-of-hospital mortality in coronary artery disease (CAD) is particularly high and established adverse event prediction tools are yet to be available. Our study aimed to investigate whether precision phenotyping can be performed using routine laboratory parameters for the prediction of out-of-hospital survival in a CAD population treated by percutaneous coronary intervention (PCI). All patients treated by PCI and discharged alive in a tertiary center between January 2016 – December 2022 that have been included prospectively in the local registry were analyzed. 115 parameters from the PCI registry and 266 parameters derived from routine laboratory testing were used. An extreme gradient-boosted decision tree machine learning (ML) algorithm was trained and used to predict all-cause and cardiovascular-cause survival. A total of 4027 patients with 4981 PCI hospitalizations were randomly included in the 70% training dataset and 1729 patients with 2160 PCI hospitalizations were randomly included in the 30% validation dataset. All-cause and cardiovascular cause mortality was 17.5% and 12.2%. The integrated area under the receiver operator characteristic curve for prediction of all-cause and cardiovascular cause mortality by the ML on the validation dataset was 0.844 and 0.837, respectively (all p < 0.001). Parameters reflecting renal function (first and maximum serum creatinine), hematologic function (mean corpuscular hemoglobin concentration, platelet distribution width), and inflammatory status (lymphocyte per monocyte ratio) were among the most important predictors. Accurate out-of-hospital survival prediction in CAD can be achieved using routine laboratory parameters. ML outperformed clinical risk scores in predicting out-of-hospital mortality in a prospective all-comers PCI population and has the potential to precisely phenotype patients.

Precision Phenotyping in Crop Science: From Plant Traits to Gene Discovery for Climate‐Smart Agriculture

ABSTRACTThe global population is placing unprecedented demand on food systems, which can be met only through a complex interplay of technology, sustainable food production intensification methods and climate resilience. To address such compounded requirements, developing high‐yielding crop varieties using precise plant breeding methods bolstered with efficient and nondestructive plant trait documentation approaches is vital. High‐throughput crop phenotyping (HTCP) platforms have prominently emerged as a mainstream approach for reducing the phenotyping bottleneck in breeding programmes. HTCP has the potential to provide detailed quantitative information of large plant populations under different growth stages across diverse environmental regimes, facilitating accelerated plant breeding strategies. New imaging platforms also enable nondestructive characterization of a wide range of above and below‐ground crop parameters. The specificity in use of sensors, automation of data collection, large‐scale data handling systems and accurate analytical tools have a substantial role in dynamic crop monitoring and big data interpretation. HTCP platforms are capable of making precise measurements of a wide range of physiological, morphological, biochemical and stress responses in plants. Developments of sensors with improved precision, intervention of unmanned aerial vehicles, robotics, computed tomography and machine learning have given a dramatic developmental leap to precise and large‐scale crop phenotyping. This review provides an avenue for understanding various high‐throughput phenotyping platforms, working principles, current developments and contributions to high‐throughput phenotyping of various crops under laboratory and field conditions. A detailed comparative idea on the advantages and pitfalls of these available platforms can help researchers in choosing the right technology suiting specific practical requirements. Furthermore, the review aims to provide novel future prospects and developmental requirements that can potentially widen the application and utilization of these HTCP technologies in agriculture.

Contrastive Functional Connectivity Defines Neurophysiology-informed Symptom Dimensions in Major Depression.

Major depressive disorder (MDD) is a prevalent psychiatric disorder characterized by substantial clinical and neurobiological heterogeneity. Conventional studies that solely focus on clinical symptoms or neuroimaging metrics often fail to capture the intricate relationship between these modalities, limiting their ability to disentangle the complexity in MDD. Moreover, patient neuroimaging data typically contains normal sources of variance shared with healthy controls, which can obscure disorder-specific variance and complicate the delineation of disease heterogeneity. We employed contrastive principal component analysis to extract disorder-specific variations in fMRI-based resting-state functional connectivity (RSFC) by contrasting MDD patients (N=233) with age-matched healthy controls (N=285). We then applied sparse canonical correlation analysis to identify latent dimensions in the disorder variations by linking the extracted contrastive connectivity features to clinical symptoms in MDD patients. Two significant and generalizable dimensions linking distinct brain circuits and clinical profiles were discovered. The first dimension, associated with an apparent "internalizing-externalizing" symptom dimension, was characterized by self-connections within the visual network and also associated with choice reaction times of cognitive tasks. The second dimension, associated with personality facets such as extraversion and conscientiousness typically inversely associated with depression symptoms, is primarily driven by self-connections within the dorsal attention network. This "depression-protective personality" dimension is also associated with multiple cognitive task performances related to psychomotor slowing and cognitive control. Our contrastive RSFC-based dimensional approach offers a new avenue to dissect clinical heterogeneity underlying MDD. By identifying two stable, neurophysiology-informed symptom dimensions in MDD patients, our findings may enhance disease mechanism insights and facilitate precision phenotyping, thus advancing the development of targeted therapeutics for precision mental health.

An Updated Comprehensive Review on Diseases Associated with Nephrotic Syndromes.

There have been exciting advances in our knowledge of primary glomerular diseases and nephrotic syndromes in recent years. Beyond the histological pattern from renal biopsy, more precise phenotyping of the diseases and the use of modern nephrogenetics helps to improve treatment decisions and sometimes also avoid unnecessary exposure to potentially toxic immunosuppression. New biomarkers have led to easier and more accurate diagnoses and more targeted therapeutic decisions. The treatment landscape is becoming wider with a pipeline of promising new therapeutic agents with more sophisticated approaches. This review focuses on all aspects of entities that are associated with nephrotic syndromes with updated information on recent advances in each field. This includes podocytopathies (focal segmental glomerulosclerosis and minimal-change disease), membranous nephropathy, membranoproliferative glomerulonephritis, IgA nephropathy, fibrillary glomerulonephritis, amyloidosis, and monoclonal gammopathy of renal significance in the context of the nephrotic syndrome, but also renal involvement in systemic diseases, diabetic nephropathy, and drugs that are associated with nephrotic syndromes.

Coronary Plaque Radiomic Phenotypes Predict Fatal or Nonfatal Myocardial Infarction: Analysis of the SCOT-HEART Trial

BackgroundCoronary computed tomography (CT) angiography–derived attenuation-based plaque burden assessments can identify patients at risk of myocardial infarction. ObjectivesThis study sought to assess whether more detailed plaque morphology assessment using patient-based radiomic characterization could further enhance the identification of patients at risk of myocardial infarction during long-term follow-up. MethodsPost hoc analysis of coronary CT angiography was performed within the SCOT-HEART (Scottish Computed Tomography of the HEART) clinical trial. Coronary plaque segmentations were used to calculate plaque burdens and eigen radiomic features that described plaque morphology. Univariable and multivariable Cox proportional hazard models were used to evaluate the association between clinical and image-based features and fatal or nonfatal myocardial infarction, whereas Harrell’s C-statistic and cumulative/dynamic area under the curve (AUC) values with cross-validation were used to evaluate prognostic performance. ResultsScans from 1,750 patients (aged 58 ± 9 years; 56% male) were analyzed. Over a median of 8.6 years of follow-up, 82 patients had a fatal or nonfatal myocardial infarction. Among the eigen radiomic features, 15 were associated with myocardial infarction in univariable analysis, and 8 features retained their association following adjustment for cardiovascular risk score and plaque burden metrics. Adding plaque burden metrics to a clinical model incorporating cardiovascular risk score, Agatston score and presence of obstructive coronary artery disease had similar prediction performance (C-statistic 0.70 vs 0.70), whereas further addition of eigen radiomic features improved model performance (C-statistic 0.74). In temporal analysis, the model including eigen radiomic features had higher cumulative/dynamic AUC values following the fifth year of follow-up. ConclusionsRadiomics-based precision phenotyping of coronary plaque morphology provided improvements to long-term prediction of myocardial infarction by CT angiography over and above clinical factors and plaque burden. (Scottish Computed Tomography of the HEART [SCOT-HEART]; NCT01149590)