Identification Of Phenotypes Research Articles

Isolation, identification and virulence gene characterization of Aeromonasdhakensis isolated from sea lion (Zalophus californianus).

Species of Aeromonas are ubiquitous pathogens of fish and aquatic animals and can infect humans and other animals through the food chain. However, there are few reports of marine mammalian infections. In 2020, a sea lion (Zalophus californianus) died acutely at an aquarium in Harbin, Heilongjiang Province, China. In order to explore the cause of death, we dissected the animal and observed pathological changes. Ogans were aseptically collected and used for bacterial isolation and culture. This revealed that the sea lion had died of sepsis caused by a bacterial infection. Isolated bacteria were investigated by morphology, biochemical phenotype and molecular identification, and this determined the pathogen as A. dhakensis. The isolate contained six virulence genes, hlyA, aerA, act, lafA, ela, fla, and was susceptible to most antibiotics. This is the first report of A. dhakensis associated with septicaemia in pinnipeds and a description of its virulence and resistance profiles. Its presence in aquatic environments poses a potential threat to marine mammals.

Paucigranulocytic Asthma: Potential Pathogenetic Mechanisms, Clinical Features and Therapeutic Management.

Asthma is a heterogeneous disease usually characterized by chronic airway inflammation, in which several phenotypes have been described, related to the age of onset, symptoms, inflammatory characteristics and treatment response. The identification of the inflammatory phenotype in asthma is very useful, since it allows for both the recognition of the asthmatic triggering factor as well as the optimization of treatment The paucigranulocytic phenotype of asthma (PGA) is characterized by sputum eosinophil levels <1–3% and sputum neutrophil levels < 60%. The precise characteristics and the pathobiology of PGA are not fully understood, and, in some cases, it seems to represent a previous eosinophilic phenotype with a good response to anti-inflammatory treatment. However, many patients with PGA remain uncontrolled and experience asthmatic symptoms and exacerbations, irrespective of the low grade of airway inflammation. This observation leads to the hypothesis that PGA might also be either a special phenotype driven by different kinds of cells, such as macrophages or mast cells, or a non-inflammatory phenotype with a low grade of eosinophilic inflammation. In this review, we aim to describe the special characteristics of PGA and the potential therapeutic interventions that could be offered to these patients.

Novel Prehospital Phenotypes and Outcomes in Adult-Patients with Acute Disease.

An early identification of prehospital phenotypes may allow health care workers to speed up and improve patients’ treatment. To determine emergency phenotypes by exclusively using prehospital clinical data, a multicenter, prospective, and observational ambulance-based study was conducted with a cohort of 3,853 adult patients treated consecutively and transferred with high priority from the scene to the hospital emergency department. Cluster analysis determined three clusters with highly different outcome scores and pathological characteristics. The first cluster presented a 30-day mortality after the index event of 45.9%. The second cluster presented a mortality of 26.3%, while mortality of the third cluster was 5.1%. This study supports the detection of three phenotypes with different risk stages and with different clinical, therapeutic, and prognostic considerations. This evidence could allow adapting treatment to each phenotype thereby helping in the decision-making process. Supplementary informationThe online version contains supplementary material available at 10.1007/s10916-022-01825-z.

Identification of Clinical Phenotypes in Septic Patients Presenting With Hypotension or Elevated Lactate.

IntroductionTargeted therapies for sepsis have failed to show benefit due to high variability among subjects. We sought to demonstrate different phenotypes of septic shock based solely on clinical features and show that these relate to outcome.MethodsA retrospective analysis was performed of a 1,023-subject cohort with early septic shock from the ProCESS trial. Twenty-three clinical variables at baseline were analyzed using hierarchical clustering, with consensus clustering used to identify and validate the ideal number of clusters in a derivation cohort of 642 subjects from 20 hospitals. Clusters were visualized using heatmaps over 0, 6, 24, and 72 h. Clinical outcomes were 14-day all-cause mortality and organ failure pattern. Cluster robustness was confirmed in a validation cohort of 381 subjects from 11 hospitals.ResultsFive phenotypes were identified, each with unique organ failure patterns that persisted in time. By enrollment criteria, all patients had shock. The two high-risk phenotypes were characterized by distinct multi-organ failure patterns and cytokine signatures, with the highest mortality group characterized most notably by liver dysfunction and coagulopathy while the other group exhibited primarily respiratory failure, neurologic dysfunction, and renal dysfunction. The moderate risk phenotype was that of respiratory failure, while low-risk phenotypes did not have a high degree of additional organ failure.ConclusionsSepsis phenotypes with distinct biochemical abnormalities may be identified by clinical characteristics alone and likely provide an opportunity for early clinical actionability and prognosis.

Epidemiology of invasive meningococcal disease in Canada, 2012-2019.

A variety of routine childhood and adolescent meningococcal vaccination programs using monovalent (serogroup C) and quadrivalent (A, C, Y, W) conjugate vaccines have been implemented in Canada since 2002, resulting in a decrease in invasive meningococcal disease (IMD) incidence, particularly in serogroup C. Meningococcal vaccines have also been used for outbreak response, including the multicomponent vaccine serogroup B vaccine. This report describes the epidemiology of IMD in Canada from 2012 to 2019. Case data were obtained from the National Enhanced IMD Surveillance System between January 1, 2012 and December 31, 2019. Isolates were sent to the National Microbiology Laboratory for confirmation of serogroup and further studies including phenotype and clonal complex identification. A total of 983 cases of IMD were reported between 2012 and 2019. Overall, the age-adjusted incidence of IMD from 2012 to 2019 was 0.34 cases per 100,000 population per year when standardized to the Canadian 2011 population age distribution (95% CI: 0.32-0.36). Infants younger than one year of age had the highest average age-specific incidence rate (3.6 cases per 100,000 population per year, 95% CI: 2.8-4.3). The highest age-adjusted incidence rate was associated with serogroup B (0.17 cases per 100, 000 population per year, 95% CI: 0.16-0.19). Prior to 2015, most invasive serogroup W isolates were identified as clonal complex 22 (ST-22 CC) and the increase in serogroup W in Canada in recent years has been associated with the replacement of the endemic ST-22 CC with the hyper-virulent ST-11 CC. Invasive meningococcal disease is a rare but severe infection in Canada that mostly affects the very young. Serogroup B continues to account for the greatest proportion of disease. Serogroup W associated with ST-11 CC is becoming a growing contributor of disease in all age groups not protected by serogroup W-containing vaccines.

Multi‐objective optimization of root phenotypes for nutrient capture using evolutionary algorithms

SUMMARYRoot phenotypes are avenues to the development of crop cultivars with improved nutrient capture, which is an important goal for global agriculture. The fitness landscape of root phenotypes is highly complex and multidimensional. It is difficult to predict which combinations of traits (phene states) will create the best performing integrated phenotypes in various environments. Brute force methods to map the fitness landscape by simulating millions of phenotypes in multiple environments are computationally challenging. Evolutionary optimization algorithms may provide more efficient avenues to explore high dimensional domains such as the root phenotypic space. We coupled the three‐dimensional functional–structural plant model, SimRoot, to the Borg Multi‐Objective Evolutionary Algorithm (MOEA) and the evolutionary search over several generations facilitated the identification of optimal root phenotypes balancing trade‐offs across nutrient uptake, biomass accumulation, and root carbon costs in environments varying in nutrient availability. Our results show that several combinations of root phenes generate optimal integrated phenotypes where performance in one objective comes at the cost of reduced performance in one or more of the remaining objectives, and such combinations differed for mobile and non‐mobile nutrients and for maize (a monocot) and bean (a dicot). Functional–structural plant models can be used with multi‐objective optimization to identify optimal root phenotypes under various environments, including future climate scenarios, which will be useful in developing the more resilient, efficient crops urgently needed in global agriculture.

Clinical sub typing of newly detected type 2 diabetics on the basis of pancreatic beta cell function and degree of insulin resistance and their clinical characterization

Background: Insulin resistance is a major cause for developing type 2 diabetes but is not synonymous with the type 2 diabetes. Pancreatic beta cell dysfunction probably needs to be sets in for clinical occurrence of type 2 diabetes. So knowledge regarding residual beta cell function and degree of insulin resistance is essential. Aims and Objectives: The objectives are as follows: (1) To estimate degree of insulin resistance (HOMA-IR) and pancreatic beta cell functional capacity (HOMA-B %) among newly detected Type 2 diabetics and (2) to identify different clinical phenotypic presentations of type 2 diabetes on the basis of these parameters. Materials and Methods: This present study was conducted in newly diagnosed type 2 diabeticpatients. After obtaining informed consent, anthropometric and clinical examination was carried out in all patients. Venous blood samples were drawn for fasting plasma glucose,c-peptide,fasting insulin level,HbA1c,lipid profile, etc.HOMA-IR and HOMA-B% were calculated with HOMA 2 calculator. Results: A total 100 newly diagnosed type 2 diabetic patients were studied. About 71% pt of study population had HOMA-B% value below 50% and half of study population had significant amount of insulin resistance. Three distinct clinical phenotypes had identified. Insulin resistance predominant group (30%), beta cell dysfunction predominant group (45%), and both abnormalities coexist group (25%). Conclusion: By the time of diagnosis of Type 2 diabetes, more than two-third study population had &lt;50% residual beta cell function left and more than half had significant degree of insulin resistance. Hence,this functional assessment needs to be done for appropriate antidiabetic drug selection and for identification of different clinical phenotypes.

Identification of autosomal recessive novel genes and retinal phenotypes in members of the solute carrier (SLC) superfamily

This study aimed to investigate the clinical and genetic aspects of solute carrier (SLC) genes in inherited retinal diseases (IRDs). Exome sequencing data were filtered to identify pathogenic variants in SLC genes. Analysis of transcript and protein expression was performed on fibroblast cell lines and retinal sections. Comprehensive analysis of 433 SLC genes in 913 exome sequencing IRD samples revealed homozygous pathogenic variants in 6 SLC genes, including 2 candidate novel genes, which were 2 variants in SLC66A1, causing autosomal recessive retinitis pigmentosa (ARRP), and a variant in SLC39A12, causing autosomal recessive mild widespread retinal degeneration with marked macular involvement. In addition, we present 4 families with ARRP and homozygous null variants in SLC37A3 that were previously suggested to cause retinitis pigmentosa, 2 of which cause exon skipping. The recently reported SLC4A7- c.2007dup variant was found in 2 patients with ARRP resulting in the absence of protein. Finally, variants in SLC24A1 were found in 4 individuals with either ARRP or congenital stationary night blindness. We report on SLC66A1 and SLC39A12 as candidate novel IRD genes, establish SLC37A3 pathogenicity, and provide further evidence of SLC4A7 as IRD genes. We extend the phenotypic spectrum of SLC24A1 and suggest that its ARRP phenotype may be more common than previously reported.

Expression of stable and reliable preference and aversion phenotypes following place conditioning with psychostimulants.

Drug-seeking behavior occurs more readily in some individuals than others. This phenomenon is considered in studies of drug self-administration in which high drug-seeking/taking individuals can be identified. In contrast, studies of conditioned place preference (CPP) often involve a random sample of drug-naïve rodents that includes phenotypes not considered relevant to addiction. The main objective of the current studies was to determine if a priori identification of different conditioning phenotypes could improve the validity and sensitivity of CPP expression as a preclinical test for vulnerability to addiction. Analysis of cocaine place conditioning data from 443 Swiss-Webster mice revealed a trimodal distribution with peaks corresponding to means of k = 3 clusters. The cluster means occurred at high, low, or negative preference scores, the latter suggesting a phenotype acquiring conditioned place aversion (CPA). The same clusters were identified in mice conditioned with methamphetamine, MDPV, or amphetamine, and these clusters remained stable and reliable during three additional expression tests spaced at 24h. A meta-analysis of effect sizes obtained from CPP literature revealed a positively skewed distribution affected by sample size, consistent with the existence of a CPA phenotype within the populations tested. A dopamine receptor antagonist, flupentixol, blocked cocaine CPP expression in a group containing all phenotypes, but sensitivity improved markedly when CPA phenotypes were excluded from the dataset. These studies suggest that taking phenotype into consideration when designing place conditioning studies will improve their application as a preclinical tool in addiction biology and drug discovery.

Identification of a novel self-compatible phenotype and its mutant SRK allele harbourin an LTR-retrotransposon in radish (Raphanus sativus L.)

ABSTRACT Self-incompatibility (SI) responses of radish (Raphanus sativus L.) are determined by two tightly linked genes encoding an S receptor kinase (SRK) and an S-locus cysteine-rich protein/S locus protein 11 (SCR/SP11), respectively. A radish showing an almost self-compatible (SC) phenotype was identified in this study. Inheritance patterns showed that this SC phenotype was dominant over an SI phenotype. In addition, the SC phenotype co-segregated with an S haplotype in an F2 population. This SC radish contained an RsS-26 haplotype in which duplicate SRK-like genes were previously identified. Full-length sequences of two SRK-like genes of 18,133-bp and 6,200-bp in length were obtained, and they were designated as RsSRK-26-1 and RsSRK-26-2, respectively. No critical mutation was found in the coding region of SRK-like genes. However, a 4,146-bp intact LTR-retrotransposon was identified in the third intron of RsSRK-26-1 of the SC radish. Interestingly, this LTR-retrotransposon was not detected in three other breeding lines containing the same RsS-26 haplotype. While normal transcription of RsSRK-26-1 and SI phenotypes were observed in these three breeding lines, no transcript of RsSRK-26-1 was detected in the SC radish, suggesting that recent transposition of an LTR-retrotransposon in the RsSRK-26-1 gene might be responsible for the SC phenotype of radish.

Quantitative CT Characteristics of Cluster Phenotypes in the Severe Asthma Research Program Cohorts.

Background Clustering key clinical characteristics of participants in the Severe Asthma Research Program (SARP), a large, multicenter prospective observational study of patients with asthma and healthy controls, has led to the identification of novel asthma phenotypes. Purpose To determine whether quantitative CT (qCT) could help distinguish between clinical asthma phenotypes. Materials and Methods A retrospective cross-sectional analysis was conducted with the use of qCT images (maximal bronchodilation at total lung capacity [TLC], or inspiration, and functional residual capacity [FRC], or expiration) from the cluster phenotypes of SARP participants (cluster 1: minimal disease; cluster 2: mild, reversible; cluster 3: obese asthma; cluster 4: severe, reversible; cluster 5: severe, irreversible) enrolled between September 2001 and December 2015. Airway morphometry was performed along standard paths (RB1, RB4, RB10, LB1, and LB10). Corresponding voxels from TLC and FRC images were mapped with use of deformable image registration to characterize disease probability maps (DPMs) of functional small airway disease (fSAD), voxel-level volume changes (Jacobian), and isotropy (anisotropic deformation index [ADI]). The association between cluster assignment and qCT measures was evaluated using linear mixed models. Results A total of 455 participants were evaluated with cluster assignments and CT (mean age ± SD, 42.1 years ± 14.7; 270 women). Airway morphometry had limited ability to help discern between clusters. DPM fSAD was highest in cluster 5 (cluster 1 in SARP III: 19.0% ± 20.6; cluster 2: 18.9% ± 13.3; cluster 3: 24.9% ± 13.1; cluster 4: 24.1% ± 8.4; cluster 5: 38.8% ± 14.4; P < .001). Lower whole-lung Jacobian and ADI values were associated with greater cluster severity. Compared to cluster 1, cluster 5 lung expansion was 31% smaller (Jacobian in SARP III cohort: 2.31 ± 0.6 vs 1.61 ± 0.3, respectively, P < .001) and 34% more isotropic (ADI in SARP III cohort: 0.40 ± 0.1 vs 0.61 ± 0.2, P < .001). Within-lung Jacobian and ADI SDs decreased as severity worsened (Jacobian SD in SARP III cohort: 0.90 ± 0.4 for cluster 1; 0.79 ± 0.3 for cluster 2; 0.62 ± 0.2 for cluster 3; 0.63 ± 0.2 for cluster 4; and 0.41 ± 0.2 for cluster 5; P < .001). Conclusion Quantitative CT assessments of the degree and intraindividual regional variability of lung expansion distinguished between well-established clinical phenotypes among participants with asthma from the Severe Asthma Research Program study. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Verschakelen in this issue.

Quantitative phenotyping and evaluation for lettuce leaves of multiple semantic components

BackgroundClassification and phenotype identification of lettuce leaves urgently require fine quantification of their multi-semantic traits. Different components of lettuce leaves undertake specific physiological functions and can be quantitatively described and interpreted using their observable properties. In particular, petiole and veins determine mechanical support and material transport performance of leaves, while other components may be closely related to photosynthesis. Currently, lettuce leaf phenotyping does not accurately differentiate leaf components, and there is no comparative evaluation for positive-back of the same lettuce leaf. In addition, a few traits of leaf components can be measured manually, but it is time-consuming, laborious, and inaccurate. Although several studies have been on image-based phenotyping of leaves, there is still a lack of robust methods to extract and validate multi-semantic traits of large-scale lettuce leaves automatically.ResultsIn this study, we developed an automated phenotyping pipeline to recognize the components of detached lettuce leaves and calculate multi-semantic traits for phenotype identification. Six semantic segmentation models were constructed to extract leaf components from visible images of lettuce leaves. And then, the leaf normalization technique was used to rotate and scale different leaf sizes to the “size-free” space for consistent leaf phenotyping. A novel lamina-based approach was also utilized to determine the petiole, first-order vein, and second-order veins. The proposed pipeline contributed 30 geometry-, 20 venation-, and 216 color-based traits to characterize each lettuce leaf. Eleven manually measured traits were evaluated and demonstrated high correlations with computation results. Further, positive-back images of leaves were used to verify the accuracy of the proposed method and evaluate the trait differences.ConclusionsThe proposed method lays an effective strategy for quantitative analysis of detached lettuce leaves' fine structure and components. Geometry, color, and vein traits of lettuce leaf and its components can be comprehensively utilized for phenotype identification and breeding of lettuce. This study provides valuable perspectives for developing automated high-throughput phenotyping application of lettuce leaves and the improvement of agronomic traits such as effective photosynthetic area and vein configuration.

Current insight into treatment of chronic rhinosinusitis: Phenotypes, endotypes, and implications for targeted therapeutics

Chronic rhinosinusitis is characterized by persistent locoregional mucosal inflammation of the paranasal sinuses and upper airway that has substantial associated health care costs. Personalized approaches to care that incorporate use of molecular biomarkers, phenotypes, and inflammatory endotypes is a major focus of research at this time, and the concurrent rise of targeted therapeutics and biologic therapies has the potential to rapidly advance care and improve outcomes. Recent findings suggest that improved understanding of chronic rhinosinusitis phenotypic and endotypic heterogeneity, and incorporation of these characteristics into clinical care pathways, may facilitate more effective selection of surgical and/or therapeutic interventions. Ultimately, these personalized approaches have the potential to target specific inflammatory pathways, increase efficacy, reduce costs, and limit side effects. This review summarizes recent advances in the identification and characterization of chronic rhinosinusitis phenotypes, endotypes, and biomarkers and reviews potential implications for targeted therapeutics.

Identification of TALE Transcription Factor Family and Expression Patterns Related to Fruit Chloroplast Development in Tomato (Solanum lycopersicum L.).

The TALE gene family is an important transcription factor family that regulates meristem formation, organ morphogenesis, signal transduction, and fruit development. A total of 24 genes of the TALE family were identified and analyzed in tomato. The 24 SlTALE family members could be classified into five BELL subfamilies and four KNOX subfamilies. SlTALE genes were unevenly distributed on every tomato chromosome, lacked syntenic gene pairs, and had conserved structures but diverse regulatory functions. Promoter activity analysis showed that cis-elements responsive to light, phytohormone, developmental regulation, and environmental stress were enriched in the promoter of SlTALE genes, and the light response elements were the most abundant. An abundance of TF binding sites was also enriched in the promoter of SlTALE genes. Phenotype identification revealed that the green shoulder (GS) mutant fruits showed significantly enhanced chloroplast development and chlorophyll accumulation, and a significant increase of chlorophyll fluorescence parameters in the fruit shoulder region. Analysis of gene expression patterns indicated that six SlTALE genes were highly expressed in the GS fruit shoulder region, and four SlTALE genes were highly expressed in the parts with less-developed chloroplasts. The protein-protein interaction networks predicted interaction combinations among these SlTALE genes, especially between the BELL subfamilies and the KNOX subfamilies, indicating a complex regulatory network of these SlTALE genes in chloroplast development and green fruit shoulder formation. In conclusion, our result provides detailed knowledge of the SlTALE gene for functional research and the utilization of the TALE gene family in fruit quality improvement.

364 Identification of Symptom-Based Phenotypes in PASC Patients through Bipartite Network Analysis: Implications for Patient Triage and Precision Treatment Strategies

OBJECTIVES/GOALS: Approximately 10% of COVID-19 patients experience multiple symptoms weeks and months after the acute phase of infection. Our goal was to use advanced machine learning methods to identify PASC phenotypes based on their symptom profiles, and their association with critical adverse outcomes, with the goal of designing future targeted interventions. METHODS/STUDY POPULATION: Data. All COVID-19 outpatients from 12 University of Minnesota hospitals and 60 clinics. Independent variables consisted of 20 CDC-defined PASC symptoms extracted from clinical notes using NLP. Covariates included demographics, and outcomes included New Psychological Diagnostic Evaluation, and Number of PASC Hospital Visits (&gt;=5). Cases (n=3235) consisted of patients with at least one symptom, and controls (n=3034) consisted of patients with no symptoms. Method. (1) Used bipartite network analysis and modularity maximization to identify patient-symptom biclusters. (2) Used multivariable logistic regression (adjusted for demographics and corrected through Bonferroni) to measure the odds ratio of each patient bicluster to adverse outcomes, compared to controls, and to each of the other biclusters. RESULTS/ANTICIPATED RESULTS: The analysis identified 6 PASC phenotypes (http://www.skbhavnani.com/DIVA/Images/Fig-1-PASC-Network.jpg), which was statistically significant compared to 1000 random permutations of the data (PASC=.31, Random Median=.27, z=11, P&lt;.01). Three of the clusters (Cluster-1, Cluster-4, and Cluster-5 encircled with ovals in Fig. 1) contained CNS-related symptoms, which had statistically significant risk for one or both of the adverse outcomes. For example, Cluster-1 with critical CNS symptoms (depression, insomnia, anxiety, brain-fog/difficulty-thinking), had a significantly higher OR compared to the controls for New Psychological Diagnostic Evaluation (OR=6.6, CI=4.9-9.1, P-corr&lt;.001), in addition to having a significantly higher ORs for the same outcome compared to all the other clusters. DISCUSSION/SIGNIFICANCE: The results identified distinct PASC phenotypes based on symptom profiles, with three of them related to CNS symptoms, each of which had significantly higher risk for specific adverse outcomes compared to controls. We will test whether these phenotypes replicate in the N3C data, and explore their translation into triage and treatment strategies.

Machine Learning Algorithms Identify Distinct Phenotypes of Right Heart Failure After Left Ventricular Assist Device Implant

<h3>Purpose</h3> The challenges of predicting right heart failure (RHF) post-Left Ventricular Assist Device (LVAD) may reflect heterogenous underlying pathophysiology. We hypothesized that 1) machine learning (ML) algorithms applied to multidimensional phenotypic data from patients with confirmed post-LVAD RHF will allow identification of distinct RHF phenotypes, 2) identified phenotypes will have unique clinical trajectories. <h3>Methods</h3> Patients with acute post-LVAD RHF (RVAD and/or ≥ 14 days inotropes post-implant, n=2,550) were identified from the ISHLT Mechanically Assisted Circulatory Support database (n=15,428); and divided into a derivation (DC, n=1,531) and validation cohort (VC, n=1,019). First, unsupervised ML (blinded to clinical outcomes) was applied to 41 pre-implant variables to identify distinct phenotypes. Then, resultant phenotypes were clinically validated by comparing outcomes of 1) RVAD/ death during index hospitalization 2) ICU Length of Stay. Results were validated in the VC. Risk discrimination of existing RHF risk scores was compared between phenotypes. <h3>Results</h3> Four distinct RHF phenotypes were identified. (Figure 1) Phenotype I had the worst, and Phenotype III had the best outcomes. Results were validated in the VC. RHF risk scores were modestly accurate at predicting RHF in those with severe shock (Phenotype I) pre-implant; but performed poorly for phenotypes without prominent shock. (Table 1) <h3>Conclusion</h3> ML identifies novel pathophysiological phenotypes of RHF, among which current risk scores were useful to predict RHF only in patients in severe shock prior to implant.

PAIN SEVERITY ACROSS HAND OSTEOARTHRITIS PHENOTYPES BASED ON A BIOPSYCHOSOCIAL APPROACH

Purpose: Hand osteoarthritis (OA) pain is both heterogeneous and multifactorial, involving biological, psychological and social aspects extending beyond the OA disease process. Different phenotypes may have different prognosis and may require different treatments. Hence, the identification of such phenotypes may be clinically important. Currently, no previous studies have examined the presence of different hand OA phenotypes. Therefore, the purpose of this study was to explore classes based on a biopsychosocial framework as possible phenotypes in a hand OA population, and examine whether pain severity differ between the classes.

Identification of CT Imaging Phenotypes of Colorectal Liver Metastases from Radiomics Signatures-Towards Assessment of Interlesional Tumor Heterogeneity.

Simple SummaryTumoral heterogeneity, which is a major challenge in therapy planning, is often characterized by genetic alterations. In this study, an image-based approach was used to identify metastases subtypes by radiomics features. Feature selection and reduction using Pearson correlation threshold and LASSO regression resulted in four final features. After unsupervised clustering and following visual assessment, five lesion clusters could be identified and defined, which had a significant (p < 0.01) correlation with sex, primary location, T- and N-status, and mutational status.(1) Background: Tumoral heterogeneity (TH) is a major challenge in the treatment of metastatic colorectal cancer (mCRC) and is associated with inferior response. Therefore, the identification of TH would be beneficial for treatment planning. TH can be assessed by identifying genetic alterations. In this work, a radiomics-based approach for assessment of TH in colorectal liver metastases (CRLM) in CT scans is demonstrated. (2) Methods: In this retrospective study, CRLM of mCRC were segmented and radiomics features extracted using pyradiomics. Unsupervised k-means clustering was applied to features and lesions. Feature redundancy was evaluated by principal component analysis and reduced by Pearson correlation coefficient cutoff. Feature selection was conducted by LASSO regression and visual analysis of the clusters by radiologists. (3) Results: A total of 47 patients’ (36% female, median age 64) CTs with 261 lesions were included. Five clusters were identified, and the categories small disseminated (n = 31), heterogeneous (n = 105), homogeneous (n = 64), mixed (n = 59), and very large type (n = 2) were assigned based on visual characteristics. Further statistical analysis showed correlation (p < 0.01) of clusters with sex, primary location, T- and N-status, and mutational status. Feature reduction and selection resulted in the identification of four features as a final set for cluster definition. (4) Conclusions: Radiomics features can characterize TH in liver metastases of mCRC in CT scans, and may be suitable for a better pretherapeutic classification of liver lesion phenotypes.

Impact of Adolescent Intermittent Ethanol Exposure on Social Investigation, Social Preference, and Neuronal Activation in cFos-LacZ Male and Female Rats.

Adolescence is a sensitive developmental period during which alcohol use is often initiated and consumed in high quantities, often at binge or even high-intensity drinking levels. Our lab has repeatedly found that adolescent intermittent ethanol (AIE) exposure in rats results in long-lasting social impairments, specifically in males, however our knowledge of the neuronal underpinnings to this sex-specific effect of AIE is limited. The present study was designed to test whether social anxiety-like alterations in AIE-exposed males would be accompanied by alterations of neuronal activation across brain regions associated with social behavior, with AIE females demonstrating no social impairments and alterations in neuronal activation. Adolescent male and female cFos-LacZ transgenic rats on a Sprague-Dawley background were exposed to ethanol (4 g/kg, 25% v/v) or water via intragastric gavage every other day during postnatal days (P) 25–45 for a total of 11 exposures (n = 13 per group). Social behavior of adult rats was assessed on P70 using a modified social interaction test, and neuronal activation in brain regions implicated in social responding was assessed via β-galactosidase (β-gal) expression. We found that AIE exposure in males resulted in a significantly lower social preference coefficient relative to water-exposed controls, with no effect evident in females. Exposure-specific relationships between social behavior and neuronal activation were identified, with AIE eliminating correlations found in water controls related to social interaction, and eliciting negative correlations mainly in limbic regions in a sex-specific manner. AIE exposure in the absence of social testing was also found to differentially affect neural activity in the orbitofrontal cortex and central amygdala in males and females. These data suggest that AIE produces sex-specific social impairments that are potentially driven by differential neuronal activation states in regions important for social behavior, including the medial prefrontal and orbitofrontal cortices, nucleus accumbens, lateral septum, and central amygdala. Future studies should be focused on identification of specific neuronal phenotypes activated by interaction with a social partner in AIE-exposed subjects and their control counterparts.

Wearable blood pressure measurement devices and new approaches in hypertension management: the digital era.

Out-of-office blood pressure (BP) measurement is considered an integral component of the diagnostic algorithm and management of hypertension. In the era of digitalization, a great deal of wearable BP measuring devices has been developed. These digital blood pressure monitors allow frequent BP measurements with minimal annoyance to the patient while they do promise radical changes regarding the diagnostic accuracy, as the importance of making an accurate diagnosis of hypertension has become evident. By increasing the number of BP measurements in different conditions, these monitors allow accurate identification of different clinical phenotypes, such as masked hypertension and pathological BP variability, that seem to have a negative impact on cardiovascular prognosis. Frequent measurements of BP and the incorporation of new features in BP variability, both enable well-rounded interpretation of BP data in the context of real-life settings. This article is a review of all different technologies and wearable BP monitoring devices.