Relevant bibliographies by topics / Phenotype

Academic literature on the topic 'Phenotype'

Author: Grafiati
Published: 4 June 2021
Last updated: 31 July 2024

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Journal articles on the topic "Phenotype"

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Moser, Lukas, Silvan Hess, Henrik Behrend, and Michael Hirschmann. "Variability of functional knee phenotypes in osteoarthritic knees shows that a more personalized approach in TKA is needed." Orthopaedic Journal of Sports Medicine 8, no. 5_suppl4 (May 1, 2020): 2325967120S0030. http://dx.doi.org/10.1177/2325967120s00300.

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Aims and Objectives: Recently, the functional knee phenotype concept was introduced as a new system to classify the coronal alignment of the lower limb. Until now, this concept has only been applied to non-osteoarthritic knees. The purpose of this study was therefore to phenotype osteoarthritic knees according to this concept and investigate the distribution of these phenotypes. Materials and Methods: Preoperative CT scans of osteoarthritic knees scheduled for TKA collected between January 2017 and December 2019 in the KneePLAN 3D database (Symbios Orthopédie S.A.) were reviewed for patients meeting the following inclusion criteria: age>50 and <90, no signs of previous fractures, osteotomies and rheumatoid arthritis. A total of 2764 patients (1438 right and 1326 left lower limbs, Male:female ratio 1096 :1668) with a mean age ± standard deviation of 70±8.5years (range 50-90 years) were included. The following coronal alignment parameters were measured using a validated software (KneePLAN 3D, Symbios Orthopédie S.A): hip-knee-ankle angle (HKA), femoral mechanical angle (FMA), and tibial mechanical angle (TMA). Based on these measurements each leg was phenotyped according to the functional knee phenotype concept and the distribution of these phenotypes assessed. A phenotype thereby consists of a phenotype specific mean value (HKA, FMA or TMA value) and covers a range of ± 1.5° from this mean (e.g. 180°± 1.5). The phenotype specific mean values represent 3° increments of the angle starting from the rounded overall mean value of the angle. Results: There were 162 different functional knee phenotypes (122 male, 138 female and 97 mutual). The most common functional knee phenotype in males was VARHKA6°VARFMA3°NEUTMA0° accounting for 8% of all males. The most common functional knee phenotype in females was VARHKA3°NEUFMA0°NEUTMA0° accounting for 9% of the population. The ten most common functional phenotypes account for 50% and 42.8% of all females and males, respectively. Overall, 134 phenotypes accounted each for less than 1% of the total population (all 134 together for 26.4%). Conclusion: The broad variability of functional knee phenotypes in osteoarthritic knees shows that a more personalized TKA realignment strategy is needed. The challenge will be to identify the optimal alignment strategy for each functional knee phenotype.
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Roemer, F., J. Collins, T. Neogi, M. Crema, and A. Guermazi. "FRI0421 RATES OF PROGRESSION DIFFER BETWEEN STRUCTURAL PHENOTYPES OF KNEE OSTEOARTHRITIS: A SECONDARY ANALYSIS FROM THE FNIH COHORT." Annals of the Rheumatic Diseases 79, Suppl 1 (June 2020): 808.1–809. http://dx.doi.org/10.1136/annrheumdis-2020-eular.1802.

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Background:Imaging plays an important role in determining structural disease severity and potential suitability of patients recruited to disease-modifying osteoarthritis drug (DMOAD) trials. It has been suggested that there may be three main structural phenotypes in OA, i.e., inflammation, meniscus/cartilage and subchondral bone. These may progress differently and may represent distinct tissue targets for DMOAD approaches.Objectives:To stratify the Foundation for National Institutes of Health Osteoarthritis Biomarkers Consortium (FNIH) cohort, a well-defined subsample of the larger Osteoarthritis Initiative (OAI) study, into distinct structural phenotypes based on semiquantitative MRI assessment and to determine their risk for progression over 48 months.Methods:The FNIH was designed as a case-control study with knees showing either 1) radiographic and pain progression (i.e., “composite” cases), 2) radiographic progression only (“JSL”), 3) pain progression only, and 4) neither radiographic nor pain progression. MRI of both knees was performed on 3 T systems at the four OAI clinical sites. Two musculoskeletal radiologists read the baseline MRIs according to the MOAKS scoring system. Knees were stratified into subchondral bone, meniscus/cartilage and inflammatory phenotypes1. A secondary, less stringent definition for inflammatory and meniscus/cartilage phenotype was used for sensitivity analyses. The relation of each phenotype to risk of being in the JSL or composite case group compared to those not having that phenotype was determined using conditional logistic regression. Only KL2 and 3 and those without root tears were included.Results:485 knees were included. 362 (75%) did not have any phenotype, while 95 (20%) had the bone phenotype, 22 (5%) the cartilage/meniscus phenotype and 19 (4%) the inflammatory phenotype. The bone phenotype was associated with a higher risk of the JSL and composite outcome (OR 1.81;[95%CI 1.14,2.85] and 1.65; 95%CI [1.04,2.61]) while the inflammatory (OR 0.96 [95%CI 0.38,2.42] and 1.25; 95%CI [0.48,3.25]) and the meniscus/cartilage phenotypes were not (OR 1.30 95%CI [0.55,3.07] and 0.99; 95%CI [0.40,2,49]).In sensitivity analyses, the bone phenotype and having two phenotypes (vs. none) were both associated with increased risk of experiencing the composite outcome (bone: OR 1.65; 95% CI 1.04, 2.61; 2 phenotypes: OR 1.87; 95% CI 1.11, 3.16.Conclusion:The bone phenotype was associated with increased risk of having both radiographic and pain progression together, or radiographic progression alone, whereas the inflammatory phenotype or meniscus/cartilage phenotype each individually were not associated with either outcome. Phenotypic stratification appears to provide insights into risk for structural or composite structure plus pain progression, and therefore may be useful to consider when selecting patients for inclusion in clinical trials.References:[1]Roemer FW, Collins J, Kwoh CK, et al. MRI-based screening for structural definition of eligibility in clinical DMOAD trials: Rapid OsteoArthritis MRI Eligibility Score (ROAMES). Osteoarthritis Cartilage 2020;28(1):71-81Disclosure of Interests:Frank Roemer: None declared, Jamie Collins Consultant of: Boston Imaging Core Lab (BICL), LLC., Tuhina Neogi Grant/research support from: Pfizer/Lilly, Consultant of: Pfizer/Lilly, EMD-Merck Serono, Novartis, Michel Crema: None declared, Ali Guermazi Consultant of: AventisGalapagos, Pfizer, Roche, AstraZeneca, Merck Serono, and TissuGene
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Carmina, Enrico, and Rogerio A. Lobo. "Comparing Lean and Obese PCOS in Different PCOS Phenotypes: Evidence That the Body Weight Is More Important Than the Rotterdam Phenotype in Influencing the Metabolic Status." Diagnostics 12, no. 10 (September 25, 2022): 2313. http://dx.doi.org/10.3390/diagnostics12102313.

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Polycystic Ovary Syndrome (PCOS) represents a heterogeneous disorder and, using Rotterdam diagnostic criteria, four main phenotypes (A, B, C, and D) have been distinguished. However, it remains unclear whether lean versus obesity status influences findings in the various phenotypes of women with PCOS. 274 women with PCOS were consecutively assessed. Among these women, there were 149 with phenotype A, 24 with phenotype B, 94 with phenotype C, and 7 with phenotype D. We found normal body weight to be very common (65%) in phenotype C patients, common (43%) in phenotype A and D patients, and less represented (but still 25%) in phenotype B patients. Obesity was common in phenotype B (54%) and phenotype A (33%) patients and uncommon in phenotype C (only 11%) and phenotype D (14%) patients. Obese and lean patients of each phenotype were compared. Compared to the phenotype C PCOS patients, both phenotype A and B patients had higher total testosterone circulating values and higher luteinizing hormone/follicle stimulating hormone (LH/FSH) ratio (p < 0.01) while anti-Mullerian hormone (AMH) levels were higher only in phenotype A PCOS patients. Instead, in the three obese PCOS phenotypes no differences in serum insulin, Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) calculation, and lipid blood values were observed. Analysis of data of lean patients gave similar results. Compared to the phenotype C PCOS patients, both phenotype A and B patients had higher total testosterone circulating values and higher LH/FSH ratio (p < 0.01) while AMH levels were higher only in phenotype A PCOS patients. However, no differences were observed in the circulating insulin levels, HOMA-IR calculation, or blood lipids between the three groups of lean PCOS patients. We conclude that Rotterdam phenotypes express the differences between PCOS patients in terms of ovulatory pattern and androgen secretion but fail to differentiate between obese patients with altered metabolic patterns and lean patients with normal metabolic patterns. A new classification of PCOS patients is needed and it should consider the influence of body weight on the metabolic patterns of PCOS patients.
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Postoeva, A. V., I. V. Dvoryashina, A. V. Kudryavtsev, and V. A. Postoev. "Prevalence of metabolic phenotypes among citizens of Arctic area of the Russian Federation (in Arkhangelsk city setting)." Obesity and metabolism 20, no. 1 (May 22, 2023): 34–42. http://dx.doi.org/10.14341/omet12926.

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BACKGROUND: Influence of obesity on the body at whole and with regard to metabolic changes is still unclear. In Russia there are a few data about prevalence of metabolic phenotypes among population based on epidemiological data.AIM: to assess the prevalence of metabolic phenotypes among citizens of Arctic area of the Russian Federation (in the Arkhangelsk city setting).MATERIALS AND METHODS: a cross-sectional study was conducted using a random sample of Arkhangelsk citizens (n=2380) 35–69 years old, which was obtained within a population study of cardiovascular diseases («Know your heart» (KYH)). The participants were divided into metabolic phenotypes according to the presence of obesity (BMI≥30 kg/m2) and metabolic syndrome (AHA/NHBLI): phenotype 1 — metabolically healthy normal weight, phenotype 2 — metabolically unhealthy normal weight, phenotype 3 — metabolically healthy obesity, phenotype 4 — metabolically unhealthy obesity.RESULTS: 2352 participants of KYH were included in the study, 982 (41,8%) men and 1370 (58,3%) women. Mean age was 53,9 (SD 9,7) years. The distribution of participants by metabolic phenotypes was as follows: 1167 (49,6%) persons had phenotype 1, 489 (20,8%) — phenotype 2, 248 (10,5%) — phenotype 3, 448 (19,1%) — phenotype 4. In men, the second common after the first phenotype was phenotype 2, while in women, the second position was shared by the 2nd and 4th phenotypes, which had approximately the same frequency. «Arterial hypertension» was the most prevalent component of metabolic syndrome and seen in 68–96% men and 38–94% women in the study with different phenotypes. The proportions of phenotypes with metabolic disorders increased with age.CONCLUSION: in a study of a random population sample within the framework of the concept of metabolic phenotypes, a half of the participants had no obesity and metabolic syndrome. Proportions of participants with metabolic disorders with and without obesity was 20% each. Only 10% of participants had «metabolically healthy» obesity. If excluding individuals without obesity and metabolic syndrome, the phenotype characterized by metabolic disorders in the absence of obesity was the most common among men. Phenotypes with metabolic disorders on the background of obesity or without obesity were equally common among women. The most common component of metabolic syndrome was «arterial hypertension». There was a tendency of accumulation of metabolic disturbances with age.
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Lusczek, Elizabeth R., Nicholas E. Ingraham, Basil S. Karam, Jennifer Proper, Lianne Siegel, Erika S. Helgeson, Sahar Lotfi-Emran, et al. "Characterizing COVID-19 clinical phenotypes and associated comorbidities and complication profiles." PLOS ONE 16, no. 3 (March 31, 2021): e0248956. http://dx.doi.org/10.1371/journal.pone.0248956.

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PurposeHeterogeneity has been observed in outcomes of hospitalized patients with coronavirus disease 2019 (COVID-19). Identification of clinical phenotypes may facilitate tailored therapy and improve outcomes. The purpose of this study is to identify specific clinical phenotypes across COVID-19 patients and compare admission characteristics and outcomes.MethodsThis is a retrospective analysis of COVID-19 patients from March 7, 2020 to August 25, 2020 at 14 U.S. hospitals. Ensemble clustering was performed on 33 variables collected within 72 hours of admission. Principal component analysis was performed to visualize variable contributions to clustering. Multinomial regression models were fit to compare patient comorbidities across phenotypes. Multivariable models were fit to estimate associations between phenotype and in-hospital complications and clinical outcomes.ResultsThe database included 1,022 hospitalized patients with COVID-19. Three clinical phenotypes were identified (I, II, III), with 236 [23.1%] patients in phenotype I, 613 [60%] patients in phenotype II, and 173 [16.9%] patients in phenotype III. Patients with respiratory comorbidities were most commonly phenotype III (p = 0.002), while patients with hematologic, renal, and cardiac (all p<0.001) comorbidities were most commonly phenotype I. Adjusted odds of respiratory, renal, hepatic, metabolic (all p<0.001), and hematological (p = 0.02) complications were highest for phenotype I. Phenotypes I and II were associated with 7.30-fold (HR:7.30, 95% CI:(3.11–17.17), p<0.001) and 2.57-fold (HR:2.57, 95% CI:(1.10–6.00), p = 0.03) increases in hazard of death relative to phenotype III.ConclusionWe identified three clinical COVID-19 phenotypes, reflecting patient populations with different comorbidities, complications, and clinical outcomes. Future research is needed to determine the utility of these phenotypes in clinical practice and trial design.
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de Koning-Tijssen, M. "One gene many phenotypes, one phenotype many genes." Journal of the Neurological Sciences 405 (October 2019): 11. http://dx.doi.org/10.1016/j.jns.2019.10.028.

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Xiromerisiou, Georgia, Henry Houlden, Nikolaos Scarmeas, Maria Stamelou, Eleanna Kara, John Hardy, Andrew J. Lees, et al. "THAP1 mutations and dystonia phenotypes: Genotype phenotype correlations." Movement Disorders 27, no. 10 (August 17, 2012): 1290–94. http://dx.doi.org/10.1002/mds.25146.

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Ferguson, Amy Christina, Sophie Thrippleton, David Henshall, Ed Whittaker, Bryan Conway, Malcolm MacLeod, Rainer Malik, et al. "Frequency and Phenotype Associations of Rare Variants in 5 Monogenic Cerebral Small Vessel Disease Genes in 200,000 UK Biobank Participants." Neurology Genetics 8, no. 5 (August 24, 2022): e200015. http://dx.doi.org/10.1212/nxg.0000000000200015.

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Background and ObjectivesBased on previous case reports and disease-based cohorts, a minority of patients with cerebral small vessel disease (cSVD) have a monogenic cause, with many also manifesting extracerebral phenotypes. We investigated the frequency, penetrance, and phenotype associations of putative pathogenic variants in cSVD genes in the UK Biobank (UKB), a large population-based study.MethodsWe used a systematic review of previous literature and ClinVar to identify putative pathogenic rare variants in CTSA, TREX1, HTRA1, and COL4A1/2. We mapped phenotypes previously attributed to these variants (phenotypes-of-interest) to disease coding systems used in the UKB's linked health data from UK hospital admissions, death records, and primary care. Among 199,313 exome-sequenced UKB participants, we assessed the following: the proportion of participants carrying ≥1 variant(s); phenotype-of-interest penetrance; and the association between variant carrier status and phenotypes-of-interest using a binary (any phenotype present/absent) and phenotype burden (linear score of the number of phenotypes a participant possessed) approach.ResultsAmong UKB participants, 0.5% had ≥1 variant(s) in studied genes. Using hospital admission and death records, 4%–20% of variant carriers per gene had an associated phenotype. This increased to 7%–55% when including primary care records. Only COL4A1 variant carrier status was significantly associated with having ≥1 phenotype-of-interest and a higher phenotype score (OR = 1.29, p = 0.006).DiscussionWhile putative pathogenic rare variants in monogenic cSVD genes occur in 1:200 people in the UKB population, only approximately half of variant carriers have a relevant disease phenotype recorded in their linked health data. We could not replicate most previously reported gene-phenotype associations, suggesting lower penetrance rates, overestimated pathogenicity, and/or limited statistical power.
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Zhou, Xue, Keijiro Nakamura, Naohiko Sahara, Masako Asami, Yasutake Toyoda, Yoshinari Enomoto, Hidehiko Hara, et al. "Exploring and Identifying Prognostic Phenotypes of Patients with Heart Failure Guided by Explainable Machine Learning." Life 12, no. 6 (May 24, 2022): 776. http://dx.doi.org/10.3390/life12060776.

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Identifying patient prognostic phenotypes facilitates precision medicine. This study aimed to explore phenotypes of patients with heart failure (HF) corresponding to prognostic condition (risk of mortality) and identify the phenotype of new patients by machine learning (ML). A unsupervised ML was applied to explore phenotypes of patients in a derivation dataset (n = 562) based on their medical records. Thereafter, supervised ML models were trained on the derivation dataset to classify these identified phenotypes. Then, the trained classifiers were further validated on an independent validation dataset (n = 168). Finally, Shapley additive explanations were used to interpret decision making of phenotype classification. Three patient phenotypes corresponding to stratified mortality risk (high, low, and intermediate) were identified. Kaplan–Meier survival curves among the three phenotypes had significant difference (pairwise comparison p < 0.05). Hazard ratio of all-cause mortality between patients in phenotype 1 (n = 91; high risk) and phenotype 3 (n = 329; intermediate risk) was 2.08 (95%CI 1.29–3.37, p = 0.003), and 0.26 (95%CI 0.11–0.61, p = 0.002) between phenotype 2 (n = 142; low risk) and phenotype 3. For phenotypes classification by random forest, AUCs of phenotypes 1, 2, and 3 were 0.736 ± 0.038, 0.815 ± 0.035, and 0.721 ± 0.03, respectively, slightly better than the decision tree. Then, the classifier effectively identified the phenotypes for new patients in the validation dataset with significant difference on survival curves and hazard ratios. Finally, age and creatinine clearance rate were identified as the top two most important predictors. ML could effectively identify patient prognostic phenotypes, facilitating reasonable management and treatment considering prognostic condition.
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Spring, Michele D., Jason C. Sousa, Qigui Li, Christian A. Darko, Meshell N. Morrison, Sean R. Marcsisin, Kristin T. Mills, et al. "Determination of Cytochrome P450 Isoenzyme 2D6 (CYP2D6) Genotypes and Pharmacogenomic Impact on Primaquine Metabolism in an Active-Duty US Military Population." Journal of Infectious Diseases 220, no. 11 (September 24, 2019): 1761–70. http://dx.doi.org/10.1093/infdis/jiz386.

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Abstract Background Plasmodium vivax malaria requires a 2-week course of primaquine (PQ) for radical cure. Evidence suggests that the hepatic isoenzyme cytochrome P450 2D6 (CYP2D6) is the key enzyme required to convert PQ into its active metabolite. Methods CYP2D6 genotypes and phenotypes of 550 service personnel were determined, and the pharmacokinetics (PK) of a 30-mg oral dose of PQ was measured in 45 volunteers. Blood and urine samples were collected, with PQ and metabolites were measured using ultraperformance liquid chromatography with mass spectrometry. Results Seventy-six CYP2D6 genotypes were characterized for 530 service personnel. Of the 515 personnel for whom a single phenotype was predicted, 58% had a normal metabolizer (NM) phenotype, 35% had an intermediate metabolizer (IM) phenotype, 5% had a poor metabolizer (PM) phenotype, and 2% had an ultrametabolizer phenotype. The median PQ area under the concentration time curve from 0 to ∞ was lower for the NM phenotype as compared to the IM or PM phenotypes. The novel 5,6-ortho-quinone was detected in urine but not plasma from all personnel with the NM phenotype. Conclusion The plasma PK profile suggests PQ metabolism is decreased in personnel with the IM or PM phenotypes as compared to those with the NM phenotype. The finding of 5,6-ortho-quinone, the stable surrogate for the unstable 5-hydroxyprimaquine metabolite, almost exclusively in personnel with the NM phenotype, compared with sporadic or no production in those with the IM or PM phenotypes, provides further evidence for the role of CYP2D6 in radical cure. Clinical Trials Registration NCT02960568.
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Dissertations / Theses on the topic "Phenotype"

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Moses, Lorraine. "Phenotypic factors influencing Mycobacterium tuberculosis phenotype." Thesis, Stellenbosch : Stellenbosch University, 2002. http://hdl.handle.net/10019.1/52997.

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Sailer, Zachary. "Predicting Phenotypes in Sparsely Sampled Genotype-Phenotype Maps." Thesis, University of Oregon, 2019. http://hdl.handle.net/1794/24231.

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Naturally evolving proteins must navigate a vast set of possible sequences to evolve new functions. This process depends on the genotype-phenotype map. Much effort has been directed at measuring protein genotype phenotype maps to uncover evolutionary trajectories that lead to new functions. Often, these maps are too large to comprehensively measure. Sparsely measured maps, however, are prone to missing key evolutionary trajectories. Many groups turn to computational models to infer missing phenotypes. These models treat mutations as independent perturbations to the genotype-phenotype map. A key question is how to handle non-independent effects known as epistasis. In this dissertation, we address two sources of epistasis: 1) global and 2) local epistasis. We find that incorporating global epistasis improves our predictive power, while local epistasis does not. We use our model to infer unknown phenotypes in the Plasmodium falciparum chloroquine transporter (PfCRT) genotype-phenotype map, a protein responsible for conferring drug resistance in malaria. From these predictions, we uncover key evolutionary trajectories that led high resistance. This dissertation includes previously published and unpublished co-authored material.
2020-01-11
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Arbon, Jed. "Phenotype-genotype correlation between the Hippo pathway and 3D craniofacial phenotypes." Thesis, University of Iowa, 2016. https://ir.uiowa.edu/etd/3042.

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Introduction: The purpose of this study was to examine phenotypic expression of craniofacial form, shape, and size as it relates to the genotype of an individual. Shape analyses were completed on 3-D images of each subject's craniofacial structure by landmarking 45 points of interest on the cranial base, facial bones, and upper and lower jaws. A candidate gene analysis was undertaken focusing on specific genes in the Hippo Signaling Pathway to examine genotype-phenotype correlations that play a role in craniofacial development. This study is a continuation of a larger project aimed at the identification of candidate genes associated with human dento-skeletal bite problems led by Dr. Lina Moreno-Uribe. Methods: The sample size for our study included 166 individuals who had never been treated orthodontically at the time of records. Each individual was genotyped and a CBCT of the craniofacial complex was captured. Each CBCT image was landmarked by a single observer using 45 points to mark points on the cranial base and facial bones including the maxilla and mandible. General Procrustes superimposition was used to find correlations with phenotype and genotype. Size analysis was completed with average Euclidean Distances and ANOVA analysis. Results: 2 SNP's from the FOX03 gene had significant associations with size. The AA genotyped individuals appeared larger in overall size than AB genotyped individuals. 3 SNP's had statistically significant associations with facial form. The FOX06 SNPs had significant associations with increased anterior-posterior growth of the maxilla. The AJUBA SNP had significant associations with increased overall craniofacial breadth. Conclusion: Genes in the Hippo signaling pathway have specific roles in the development of facial form and size.
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Habib, Farhat Abbas. "Genotype-phenotype correlation using phylogenetic trees." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1187297400.

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Bloomfield, Kelly Louise, and n/a. "Investigation of the Role of Thioredoxin in the Invasive Phenotype and its Interaction with the Transcription Factor Sp1." Griffith University. School of Biomolecular and Biomedical Science, 2003. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20031021.120018.

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Thioredoxin is a small ubiquitous oxido-reductase found in all species. The highly conserved active site, which facilitates thioredoxins redox activity, contains two redox active cysteine residues. Thioredoxin has numerous protein substrates to which it donates H+ ions and it can also function as a free radical scavenger. Through these activities thioredoxin is able to influence the redox state of not only its protein targets, but also the entire cellular environment. Thioredoxin has been implicated in many biological functions, and one mechanism by which it influences these functions is through interactions with a number of transcription factors including NF-kappa-B and p53. Thioredoxin also has numerous extracellular biological roles. It has been shown that thioredoxin is actively secreted from a number of normal and transformed cell lines including fibroblasts and activated B and T cells. This study investigates the role of thioredoxin in embryonic implantation and cancer cell metastasis, two physiological functions which rely on the same basic processes. Thioredoxin expression has previously been shown to be increased in many cancers. However it has not yet been established whether this increase is a causative or a side effect of the cancerous phenotype. Similarly thioredoxin expression has previously been shown to be increased during different phases of the oestrus cycle and pregnancy. This thesis describes the role of thioredoxin in embryonic implantation using a marmoset model. A thioredoxin cDNA was isolated and subsequently sequenced. Preliminary antibody experiments indicated that the anti human thioredoxin monoclonal antibodies available in our laboratory would recognise marmoset thioredoxin. Subsequently immunocytochemistry using anti human thioredoxin antibodies was carried out on sectioned marmoset uterus and embryonic tissue. The results indicated that thioredoxin is expressed by cells at the embryonic-maternal interface of early implantation sites. Further studies demonstrated that thioredoxin is also expressed and secreted by cultured blastocysts in vitro. This thesis also describes the role of thioredoxin in cancer cell metastasis. Results of this study indicate that thioredoxin is actively involved in facilitating the invasive phenotype of breast cancer cells. The two cell lines utilised were MCF-7, a well differentiated, relatively non-invasive breast cancer cell line; and MDA-MB-231, a poorly differentiated, highly invasive breast cancer cell line. The cell lines were transfected with thioredoxin sense, antisense and 1SS (encodes thioredoxin with both active cysteine residues mutated to serine residues and is thus redox inactive) constructs. The results demonstrate that when endogenous thioredoxin levels are increased, i.e. transfected with a sense thioredoxin construct, the invasive breast cancer cell line MDA-MB-231 becomes more invasive, conversely when endogenous levels are decreased, i.e. transfected with antisense or 1SS constructs, the invasive capacity of these cells decreases. However, when the endogenous level of thioredoxin was manipulated in the relatively non-invasive cell line MCF-7 very little effect was observed. Results also indicate that thioredoxin has the ability to act as a chemoattractant for actively invading breast cancer cells. Both of these functions appear to be dependent on thioredoxin's redox activity. Additional studies described in this thesis have shown that thioredoxin is involved in the regulation of Sp1 in vitro. Sp1 is a transcription factor known to regulate the transcription of a number of genes whose products are intimately involved in the invasive phenotype. The results in this study suggest that Sp1 DNA binding is regulated by thioredoxin such that when reduced by the enzyme its binding to DNA is facilitated. Results also indicate that Sp1 may regulate the transcription of thioredoxin by binding to Sp1 sites within the thioredoxin promoter.
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Bloomfield, Kelly Louise. "Investigation of the Role of Thioredoxin in the Invasive Phenotype and its Interaction with the Transcription Factor Sp1." Thesis, Griffith University, 2003. http://hdl.handle.net/10072/366170.

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Thioredoxin is a small ubiquitous oxido-reductase found in all species. The highly conserved active site, which facilitates thioredoxins redox activity, contains two redox active cysteine residues. Thioredoxin has numerous protein substrates to which it donates H+ ions and it can also function as a free radical scavenger. Through these activities thioredoxin is able to influence the redox state of not only its protein targets, but also the entire cellular environment. Thioredoxin has been implicated in many biological functions, and one mechanism by which it influences these functions is through interactions with a number of transcription factors including NF-_B and p53. Thioredoxin also has numerous extracellular biological roles. It has been shown that thioredoxin is actively secreted from a number of normal and transformed cell lines including fibroblasts and activated B and T cells. This study investigates the role of thioredoxin in embryonic implantation and cancer cell metastasis, two physiological functions which rely on the same basic processes. Thioredoxin expression has previously been shown to be increased in many cancers. However it has not yet been established whether this increase is a causative or a side effect of the cancerous phenotype. Similarly thioredoxin expression has previously been shown to be increased during different phases of the oestrus cycle and pregnancy. This thesis describes the role of thioredoxin in embryonic implantation using a marmoset model. A thioredoxin cDNA was isolated and subsequently sequenced. Preliminary antibody experiments indicated that the anti human thioredoxin monoclonal antibodies available in our laboratory would recognise marmoset thioredoxin. Subsequently immunocytochemistry using anti human thioredoxin antibodies was carried out on sectioned marmoset uterus and embryonic tissue. The results indicated that thioredoxin is expressed by cells at the embryonic-maternal interface of early implantation sites. Further studies demonstrated that thioredoxin is also expressed and secreted by cultured blastocysts in vitro. This thesis also describes the role of thioredoxin in cancer cell metastasis. Results of this study indicate that thioredoxin is actively involved in facilitating the invasive phenotype of breast cancer cells. The two cell lines utilised were MCF-7, a well differentiated, relatively non-invasive breast cancer cell line; and MDA-MB-231, a poorly differentiated, highly invasive breast cancer cell line. The cell lines were transfected with thioredoxin sense, antisense and 1SS (encodes thioredoxin with both active cysteine residues mutated to serine residues and is thus redox inactive) constructs. The results demonstrate that when endogenous thioredoxin levels are increased, i.e. transfected with a sense thioredoxin construct, the invasive breast cancer cell line MDA-MB-231 becomes more invasive, conversely when endogenous levels are decreased, i.e. transfected with antisense or 1SS constructs, the invasive capacity of these cells decreases. However, when the endogenous level of thioredoxin was manipulated in the relatively non-invasive cell line MCF-7 very little effect was observed. Results also indicate that thioredoxin has the ability to act as a chemoattractant for actively invading breast cancer cells. Both of these functions appear to be dependent on thioredoxin's redox activity. Additional studies described in this thesis have shown that thioredoxin is involved in the regulation of Sp1 in vitro. Sp1 is a transcription factor known to regulate the transcription of a number of genes whose products are intimately involved in the invasive phenotype. The results in this study suggest that Sp1 DNA binding is regulated by thioredoxin such that when reduced by the enzyme its binding to DNA is facilitated. Results also indicate that Sp1 may regulate the transcription of thioredoxin by binding to Sp1 sites within the thioredoxin promoter.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Biomolecular and Biomedical Sciences
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Ried, Janina S. "Phenotype set enrichment analysis." Diss., Ludwig-Maximilians-Universität München, 2013. http://nbn-resolving.de/urn:nbn:de:bvb:19-158079.

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Alsbou, Mohammed. "Dissecting phenotype-genoype relationships." Thesis, University of Liverpool, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.443925.

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ARDISSONE, ANNA. "Mitochondrial diseases related to mtDNA in childhood: genotype-phenotype correlation and characterization of novel phenotypes." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2020. http://hdl.handle.net/10281/262917.

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Le malattie mitocondriali (MD) sono un gruppo clinicamente eterogeneo di patologie dovute al difetto di funzione dei mitocondri, in particolare della catena respiratoria mitocondriale (RC) e della fosforilazione ossidativa (OXPHOS). Le funzioni mitocondriali sono sotto il controllo di due diversi genomi: DNA mitocondriale (mtDNA) e genoma nucleare (nDNA). Le forme infantili sono più spesso associate a mutazioni nel nDNA; negli ultimi anni tecnologie di sequenziamento di nuova generazione (Next Generation Sequencing-NGS) hanno permesso di identificare nuovi geni malattia; in collaborazione con altri centri abbiamo contribuito alla definizione del fenotipo associato ai nuovi geni malattia identificati. L’applicazione di tale tecnica è stata estesa anche allo studio del mtDNA: anche se sono state descritte più di 100 mutazioni e delezioni nel mtDNA in associazione a uno spettro estremamente eterogeneo di presentazioni cliniche, solo alcune di esse sono associate a sindromi cliniche ben definite nell'infanzia. Abbiamo effettuato una valutazione sistematica dei dati clinici, strumentali, metabolici e biochimici di una vasta coorte di pazienti affetti dal MD più comune nell'infanzia: la sindrome di Leigh. Abbiamo analizzato in questa popolazione la correlazione genotipo-fenotipo nei casi associati al gene nDNA e mtDNA al fine di identificare indizi diagnostici per la sindrome di Leigh correlata al mtDNA. In casi geneticamente irrisolti e vari fenotipi (sindrome di Leigh, leucodistrofia ...), abbiamo eseguito lo screening del mtDNA utilizzando tecnologie di sequenziamento di nuova generazione (NGS) al fine di valutare, con elevata precisione, mutazioni puntiformi e delezioni singole o multiple di grandi dimensioni, entrambe in omoplasma o stato eteroplasmico. Abbiamo identificato mutazioni sia nuove che note associate a fenotipo inatteso (es. MTCO3). I nostri dati definiscono e ampliano meglio lo spettro fenotipico del mtDNA-MD nell'infanzia.
Mitochondrial diseases (MD) are a clinically heterogeneous group of disorders that arise as a result of dysfunction of the mitochondrial respiratory chain (RC) and oxidative phosphorylation (OXPHOS). Mitochondrial functions are under the control of two different genomes: mitochondrial DNA (mtDNA) and nuclear genome (nDNA). Childhood phenotypes are often associated with nDNA mutations; in recent years, new-generation sequencing technologies (Next Generation Sequencing-NGS) have identified novel causative genes; in collaboration with other centers we contributed to the definition of phenotype associated with the new identified disease genes. The application of this technique has also been extended to the study of mtDNA: even if more than 100 mutations and deletions in mtDNA have been described in association with an extremely heterogeneous spectrum of clinical presentations, only a few of them are associated with well-defined clinical syndromes in childhood. We performed a systematic evaluation of clinical, instrumental, metabolic and biochemical data of a large cohort of patients affected by the most common MD in childhood: Leigh syndrome. We analyzed in this population, genotype-phenotype correlation in nDNA and mtDNA gene associated cases in order to identify diagnostic clues for mtDNA related Leigh syndrome. In genetically unresolved cases and various phenotypes (Leigh syndrome, leukodystropy..), we performed mtDNA screening using next-generation sequencing (NGS) technologies in order to assess, with high accuracy, point mutations and single or multiple large deletions, both in homoplasmic or heteroplasmic state. We identified both novel and known mutations associated to unexpected phenotype (i.e. CO3 gene). Our data better define and expand the phenotypic spectrum of mtDNA-MD in childhood.
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Gale, Christopher Robert Keith. "Newborn feeding and infant phenotype." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/39361.

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Breastfeeding in infancy, when compared with formula feeding, is associated with a reduced incidence of components of the metabolic syndrome later in life. One potential mechanism is via an effect on lipid metabolism and storage, manifesting as altered adiposity and ectopic lipid deposition. I have examined the null hypothesis: no association is detectable between infant feeding and adiposity or ectopic lipid in infancy, through a meta-analysis of published studies and a prospective cohort study of healthy infants employing gold standard direct measurement techniques (magnetic resonance imaging and spectroscopy). Eleven studies were identified for meta-analysis: in formula-fed compared to breastfed infants, fat mass was lower at 3-4 months [mean difference (95% confidence interval)]: [-0.09 kg (-0.18, -0.01 kg)] and 6 months [-0.18 kg (- 0.34, -0.01 kg)]. Conversely, at 12 months, fat mass was higher in formula-fed infants [0.29 kg (-0.03, 0.61 kg)] than in breastfed infants. Eighty-seven infants were included in a prospective cohort, of which 73 were investigated at two time points. In healthy, term, breastfed infants adipose tissue accretion between birth and 2-3 months ages was predominantly within subcutaneous rather than internal adipose tissue compartments, and a significant increase in intrahepatocellular lipid was detected: median [interquartile range] 0.653 [0.367-1.900] after birth and 1.837 [1.408-2.429] at 2-3 months. Comparing breastfed with formula fed infants within this cohort no significant differences were detected in total adipose tissue, adipose tissue distribution or intrahepatocellular lipid between birth and 2-3 months. Significant associations were detected between maternal BMI, rate of weight gain in early infancy and gender, and adipose tissue partitioning at 2-3 months. While method of feeding is associated with altered infant fat mass up to 6 months, no association is detectable with adipose tissue partitioning or ectopic hepatic lipid at 2-3 months.
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Books on the topic "Phenotype"

1

Zielonka, Stefan, and Simon Krah, eds. Genotype Phenotype Coupling. New York, NY: Springer US, 2020. http://dx.doi.org/10.1007/978-1-4939-9853-1.

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Zielonka, Stefan, and Simon Krah, eds. Genotype Phenotype Coupling. New York, NY: Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-3279-6.

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Sue, Malcolm, and Goodship Timothy H. J, eds. Genotype to phenotype. 2nd ed. Oxford: BIOS Scientific, 2001.

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van, Gils Jan A., ed. The flexible phenotype: Towards a body-centred integration of ecology, physiology, and behaviour. Oxford: Oxford University Press, 2010.

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Feldbauer, Roman. Machine Learning for Microbial Phenotype Prediction. Wiesbaden: Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-14319-0.

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Savagner, Pierre. Rise and Fall of Epithelial Phenotype. Boston, MA: Springer US, 2005. http://dx.doi.org/10.1007/0-387-28671-3.

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1947-, Friedman J. M., and Riccardi Vincent M. 1940-, eds. Neurofibromatosis: Phenotype, natural history, and pathogenesis. 3rd ed. Baltimore: Johns Hopkins University Press, 1999.

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E, Eichner June, ed. Neurofibromatosis: Phenotype, natural history, and pathogenesis. Baltimore: Johns Hopkins University Press, 1986.

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editor, Fielding Roger A., Sieber Cornel editor, Vellas, B. J. (Bruno J.), editor, and Nestlé Nutrition Institute, eds. Frailty: Pathophysiology, phenotype and patient care. Basel, Switzerland: Karger, 2015.

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Pontarotti, Pierre, ed. Evolutionary Biology: Biodiversification from Genotype to Phenotype. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19932-0.

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Book chapters on the topic "Phenotype"

1

Frank, J. Howard, J. Howard Frank, Michael C. Thomas, Allan A. Yousten, F. William Howard, Robin M. Giblin-davis, John B. Heppner, et al. "Phenotype." In Encyclopedia of Entomology, 2841. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-6359-6_2899.

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Hahn, Martin, and Rohan Palmer. "Phenotype." In Encyclopedia of Clinical Neuropsychology, 2673. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-57111-9_1861.

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Manrubia, Susanna C. "Phenotype." In Encyclopedia of Astrobiology, 1224–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-11274-4_1179.

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Hahn, Martin, and Rohan Palmer. "Phenotype." In Encyclopedia of Clinical Neuropsychology, 1934. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-0-387-79948-3_1861.

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McCarthy, Jeanette. "Phenotype." In Encyclopedia of Behavioral Medicine, 1471–72. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1005-9_713.

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Nahler, Gerhard. "phenotype." In Dictionary of Pharmaceutical Medicine, 140. Vienna: Springer Vienna, 2009. http://dx.doi.org/10.1007/978-3-211-89836-9_1061.

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Hahn, Martin, and Rohan Palmer. "Phenotype." In Encyclopedia of Clinical Neuropsychology, 1. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56782-2_1861-2.

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Manrubia, Susanna. "Phenotype." In Encyclopedia of Astrobiology, 1851–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-44185-5_1179.

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Tsui, Nancy B. Y., and Johnson Y. N. Lau. "Phenotype." In Encyclopedia of Gerontology and Population Aging, 1–5. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-69892-2_939-1.

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Sander, Jana. "Phenotype." In Encyclopedia of Child Behavior and Development, 1084. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-79061-9_2134.

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Conference papers on the topic "Phenotype"

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Liu, Weizhen, Jiayu Tan, Guangyu Lan, Ao Li, Dongye Li, Le Zhao, Xiaohui Yuan, and Nanqing Dong. "Benchmarking Fish Dataset and Evaluation Metric in Keypoint Detection - Towards Precise Fish Morphological Assessment in Aquaculture Breeding." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. California: International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/816.

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Accurate phenotypic analysis in aquaculture breeding necessitates the quantification of subtle morphological phenotypes. Existing datasets suffer from limitations such as small scale, limited species coverage, and inadequate annotation of keypoints for measuring refined and complex morphological phenotypes of fish body parts. To address this gap, we introduce FishPhenoKey, a comprehensive dataset comprising 23,331 high-resolution images spanning six fish species. Notably, FishPhenoKey includes 22 phenotype-oriented annotations, enabling the capture of intricate morphological phenotypes. Motivated by the nuanced evaluation of these subtle morphologies, we also propose a new evaluation metric, Percentage of Measured Phenotypes (PMP). It is designed to assess the accuracy of individual keypoint positions and is highly sensitive to the phenotype measured using the corresponding keypoints. To enhance keypoint detection accuracy, we further propose a novel loss, Anatomically-Calibrated Regularization (ACR), that can be integrated into keypoint detection models, leveraging biological insights to refine keypoint localization. Our contributions set a new benchmark in fish phenotype analysis, addressing the challenges of precise morphological quantification and opening new avenues for research in sustainable aquaculture and genetic studies. Our dataset and code are available at https://github.com/WeizhenLiuBioinform/FishPhenotype-Detect.
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Hartmann, Morten, and Tim Goedeweeck. "Adapting a Genotype-phenotype Mapping to Phenotypic Complexity." In 2009 NASA/ESA Conference on Adaptive Hardware and Systems (AHS). IEEE, 2009. http://dx.doi.org/10.1109/ahs.2009.47.

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Peng, Jiajie, Weiwei Hui, and Xuequn Shang. "Measuring phenotype-phenotype similarity through the interactome." In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2017. http://dx.doi.org/10.1109/bibm.2017.8217911.

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Tsvelaya, V. A., M. M. Slotvitsky, A. A. Aitova, S. A. Romanova, and K. I. Agladze. "TISSUE ENGINEERING AS A TOOL FOR RESEARCH AND DEVELOPMENT OF POTENTIAL THERAPIES FOR THE PROCESSES OF ARRHYTHMIAS." In X Международная конференция молодых ученых: биоинформатиков, биотехнологов, биофизиков, вирусологов и молекулярных биологов — 2023. Novosibirsk State University, 2023. http://dx.doi.org/10.25205/978-5-4437-1526-1-219.

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At the moment, there is a lot of controversy about the dependence of cardiovascular diseases on the phenotype of heart cells obtained at the stage of heart formation and during its development throughout the life of the patient. One of the tasks of modern medicine, therefore, can be considered a comparison of factors leading to a pathological phenotype, depending on the genotype and regardless of it. This work is a study of the formation of cardiac tissue and the electrophysiological properties of cardiomyocytes under various conditions that determine the phenotypic manifestations of functional differences in cardiomyocytes during the maturation of cardiac tissue in vitro.
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Peng, Jiajie, Hansheng Xue, Yukai Shao, Xuequn Shang, Yadong Wang, and Jin Chen. "Measuring phenotype semantic similarity using Human Phenotype Ontology." In 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2016. http://dx.doi.org/10.1109/bibm.2016.7822617.

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Morales, F. L., C. Gao, T. Stoeger, A. Pawlowski, P. Nannapaneni, D. H. Schneider, M. Kang, et al. "Capturing Phenotypic Manifestations of Severe Pneumonia Through the Human Phenotype Ontology." In American Thoracic Society 2022 International Conference, May 13-18, 2022 - San Francisco, CA. American Thoracic Society, 2022. http://dx.doi.org/10.1164/ajrccm-conference.2022.205.1_meetingabstracts.a5150.

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Otter, Tim. "Genotype, phenotype and ontogeny." In the 2005 workshops. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1102256.1102323.

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GKOUTOS, G. V., E. C. J. GREEN, A. M. MALLON, J. M. HANCOCK, and D. DAVIDSON. "BUILDING MOUSE PHENOTYPE ONTOLOGIES." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 2003. http://dx.doi.org/10.1142/9789812704856_0018.

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WATERS, DENNIS P. "LANGUAGE AS EXTENDED PHENOTYPE?" In Proceedings of the 8th International Conference (EVOLANG8). WORLD SCIENTIFIC, 2010. http://dx.doi.org/10.1142/9789814295222_0122.

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Ferri, Sebastian, Ramona Sorrentino, Carlo Chessari, Giovanni Terranova, Teresa Augelletti, Raffaele Campisi, Enrico Heffler, Nunzio Crimi, and Sabrina Genco. "Bronchiectasis: understanding the phenotype." In ERS International Congress 2017 abstracts. European Respiratory Society, 2017. http://dx.doi.org/10.1183/1393003.congress-2017.pa3594.

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Reports on the topic "Phenotype"

1

Daniel W McDonald and Ronald B Michaels. Plant Phenotype Characterization System. Office of Scientific and Technical Information (OSTI), September 2005. http://dx.doi.org/10.2172/850165.

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Dubbin, K. R., D. R. Gray, K. A. Anderson, and M. L. Moya. Metamaterials for Mechanically Directing Cell Phenotype. Office of Scientific and Technical Information (OSTI), April 2020. http://dx.doi.org/10.2172/1614964.

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Koutcher, Jason. Imaging Prostate Cancer (Pca) Phenotype and Evolution. Fort Belvoir, VA: Defense Technical Information Center, October 2014. http://dx.doi.org/10.21236/ada612861.

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Walker, Cheryl. TSC2 Happloinsufficiency Leads to a Mutator Phenotype. Fort Belvoir, VA: Defense Technical Information Center, November 2007. http://dx.doi.org/10.21236/ada481229.

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Lelievre, Sophie A. How Does Nuclear Organization Maintain Normal Mammary Phenotype? Fort Belvoir, VA: Defense Technical Information Center, March 2005. http://dx.doi.org/10.21236/ada436884.

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Leievre, Sophie A. How Does Nuclear Organization Maintain Normal Mammary Phenotype. Fort Belvoir, VA: Defense Technical Information Center, July 2001. http://dx.doi.org/10.21236/ada396710.

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Martin, Sandra L., and Hannah V. Carey. Translating the Hibernation Phenotype to Human Trauma Care. Fort Belvoir, VA: Defense Technical Information Center, September 2008. http://dx.doi.org/10.21236/ada500737.

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Lelievre, Sophie A. How Does Nuclear Organization Maintain Normal Mammary Phenotype. Fort Belvoir, VA: Defense Technical Information Center, July 2002. http://dx.doi.org/10.21236/ada408754.

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Wilson, Sarah, Ingrid Scheffer, Valerie Yap, Alan Connelly, and Neil McLachlan. Identifying Neurobiological Markers of the Broader Autism Phenotype. Fort Belvoir, VA: Defense Technical Information Center, September 2013. http://dx.doi.org/10.21236/ada601230.

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Wilson, Sarah, Ingrid Scheffer, and Valerie Yap. Identifying Neurobiological Markers of the Broader Autism Phenotype. Fort Belvoir, VA: Defense Technical Information Center, January 2015. http://dx.doi.org/10.21236/ada621217.

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