71 research outputs found
Identification of a variant hotspot in MYBPC3 and of a novel CSRP3 autosomal recessive alteration in a cohort of Polish patients with hypertrophic cardiomyopathy
INTRODUCTION Hypertrophic cardiomyopathy (HCM) is a heart disorder caused by autosomal dominant alterations affecting both sarcomeric genes and other nonsarcomeric loci in a minority of cases. However, in some patients, the occurrence of the causal pathogenic variant or variants in homozygosity, compound heterozygosity, or double heterozygosity has also been described. Most of the HCM pathogenic variants are missense and unique, but truncating mutations of the MYBPC3 gene have been reported as founder pathogenic variants in populations from Finland, France, Japan, Iceland, Italy, and the Netherlands. OBJECTIVES This study aimed to assess the genetic background of HCM in a cohort of Polish patients. PATIENTS AND METHODS Twentyβnine Polish patients were analyzed by a nextβgeneration sequencing panel including 404 cardiovascular genes. RESULTS Pathogenic variants were found in 41% of the patients, with ultraβrare MYBPC3 c.2541C>G (p.Tyr847Ter) mutation standing for a variant hotspot and correlating with a lower age at HCM diagnosis. Among the nonsarcomeric genes, the CSRP3 mutation was found in a single case carrying the novel c.364C>T (p.Arg122Ter) variant in homozygosity. With this finding, the total number of known HCM cases with human CSRP3 knockout cases has reached 3
Π§Π΅ΡΡΡΠ΅ ΠΊΠ»Π΅ΡΠΎΡΠ½ΠΎ-Π°Π²ΡΠΎΠΌΠ°ΡΠ½ΡΡ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΏΠ΅ΡΠΌΡΡΠ°ΡΠΈΠΉ ΠΌΠ°ΡΡΠΈΡ
Numerical calculation uses to describe the operation of matrix permutation algorithms based on cyclic shifts of rows and columns. This choice of discrete transformation algorithms justified by the convenience of the cellular automaton (CA) formulation, which is used. Obtained Empirical formulas for the permutation period and for the last algorithm, which period formula is recurrent. For a base scheme period has the asymptotics: Β for a matrix Β with pairwise different elements. Despite the complexity of the scheme, the other two modifications only give a polynomial growth of period, no higher than 3. Fourth scheme has a non-trivial period dependence, but no higher than the exponential. In some cases algorithms make special permutations: rotate, reflect, and rearrange blocks for the matrix . These formulas are closely related to individual cells paths. And paths connected with the influence of the boundaries that gives branching the matrix order by subtraction class modulo 3,4 or 12. Visualizations of these paths make in the extended CA-field. Two "mixing metrics" analyze as a parameter of CA dynamics on matrix permutations (compared to the initial). For all schemes and most branches, the behavior of these metrics shows in graphs and histograms (conditional density distribution) showing how often the permutation period occurs with the specified interval of metrics. The practical aim of this work is in the field of pseudorandom number generation and cryptography.Π‘ ΠΏΠΎΠΌΠΎΡΡΡ ΡΠΈΡΠ»Π΅Π½Π½ΠΎΠ³ΠΎ ΡΠ°ΡΡΠ΅ΡΠ° ΠΎΠΏΠΈΡΡΠ²Π°Π΅ΡΡΡ ΡΠ°Π±ΠΎΡΠ° Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠ² ΠΏΠ΅ΡΠΌΡΡΠ°ΡΠΈΠΉ ΠΌΠ°ΡΡΠΈΡ, ΠΎΡΠ½ΠΎΠ²Π°Π½Π½ΡΡ
Π½Π° ΡΠΈΠΊΠ»ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ΄Π²ΠΈΠ³Π°Ρ
ΡΡΡΠΎΠΊ ΠΈ ΡΡΠΎΠ»Π±ΡΠΎΠ². Π’Π°ΠΊΠΎΠΉ Π²ΡΠ±ΠΎΡ Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠ² Π΄ΠΈΡΠΊΡΠ΅ΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ ΠΎΠ±ΠΎΡΠ½ΠΎΠ²Π°Π½ ΡΠ΄ΠΎΠ±ΡΡΠ²ΠΎΠΌ ΠΊΠ»Π΅ΡΠΎΡΠ½ΠΎ-Π°Π²ΡΠΎΠΌΠ°ΡΠ½ΡΡ
ΡΠΎΡΠΌΡΠ»ΠΈΡΠΎΠ²ΠΎΠΊ, ΠΊΠΎΡΠΎΡΡΠ΅ ΠΈ ΠΏΡΠΈΠ²ΠΎΠ΄ΡΡΡΡ. ΠΠΎΠ»ΡΡΠ΅Π½Ρ ΡΠΌΠΏΠΈΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΡΠΎΡΠΌΡΠ»Ρ Π΄Π»Ρ ΠΏΠ΅ΡΠΈΠΎΠ΄Π° ΠΏΠ΅ΡΠΌΡΡΠ°ΡΠΈΠΉ; Π΄Π»Ρ ΠΏΠΎΡΠ»Π΅Π΄Π½Π΅Π³ΠΎ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΡΠΎΡΠΌΡΠ»Π° ΠΏΠ΅ΡΠΈΠΎΠ΄Π° Π½ΠΎΡΠΈΡ ΡΠ΅ΠΊΡΡΡΠ΅Π½ΡΠ½ΡΠΉ Ρ
Π°ΡΠ°ΠΊΡΠ΅Ρ. ΠΠ»Ρ Π±Π°Π·ΠΎΠ²ΠΎΠΉ ΠΈ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΠΏΡΠΎΡΡΠΎΠΉ ΡΡ
Π΅ΠΌΡ ΠΏΠ΅ΡΠΈΠΎΠ΄ N(n) ΠΈΠΌΠ΅Π΅Ρ Π°ΡΠΈΠΌΠΏΡΠΎΡΠΈΠΊΡ exp(2n)/n Π΄Π»Ρ ΠΌΠ°ΡΡΠΈΡΡ nxn Ρ ΠΏΠΎΠΏΠ°ΡΠ½ΠΎ ΡΠ°Π·Π»ΠΈΡΠ½ΡΠΌΠΈ ΡΠ»Π΅ΠΌΠ΅Π½ΡΠ°ΠΌΠΈ. ΠΠ΅ΡΠΌΠΎΡΡΡ Π½Π° ΡΡΠ»ΠΎΠΆΠ½Π΅Π½ΠΈΠ΅ ΡΡ
Π΅ΠΌΡ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ°, Π΄Π²Π΅ Π΄ΡΡΠ³ΠΈΠ΅ ΠΌΠΎΠ΄ΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ Π΄Π°ΡΡ Π»ΠΈΡΡ ΠΏΠΎΠ»ΠΈΠ½ΠΎΠΌΠΈΠ°Π»ΡΠ½ΡΠΉ ΡΠΎΡΡ ΡΡΠ΅ΠΏΠ΅Π½ΠΈ Π½Π΅ Π²ΡΡΠ΅ 3. Π§Π΅ΡΠ²Π΅ΡΡΠ°Ρ ΡΡ
Π΅ΠΌΠ° ΠΈΠΌΠ΅Π΅Ρ Π½Π΅ΡΡΠΈΠ²ΠΈΠ°Π»ΡΠ½ΡΡ Π·Π°Π²ΠΈΡΠΈΠΌΠΎΡΡΡ ΠΏΠ΅ΡΠΈΠΎΠ΄Π°, Π½ΠΎ Π½Π΅ Π²ΡΡΠ΅ ΡΠΊΡΠΏΠΎΠ½Π΅Π½ΡΠΈΠ°Π»ΡΠ½ΠΎΠΉ. Π ΡΡΠ΄Π΅ ΡΠ»ΡΡΠ°Π΅Π² Π°Π»Π³ΠΎΡΠΈΡΠΌΡ ΠΏΠΎΡΠΎΠΆΠ΄Π°ΡΡ ΠΎΡΠΎΠ±ΡΠ΅ ΠΏΠ΅ΡΠΌΡΡΠ°ΡΠΈΠΈ: ΠΏΠΎΠ²ΠΎΡΠΎΡ, ΠΎΡΡΠ°ΠΆΠ΅Π½ΠΈΠ΅ ΠΈ ΠΏΠ΅ΡΠ΅ΡΡΠ°Π½ΠΎΠ²ΠΊΡ Π±Π»ΠΎΠΊΠΎΠ² Π΄Π»Ρ ΠΌΠ°ΡΡΠΈΡΡ 2kx2k. ΠΡΠΈ ΡΠΎΡΠΌΡΠ»Ρ ΡΠ΅ΡΠ½ΠΎ ΡΠ²ΡΠ·Π°Π½Ρ Ρ ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΡΠ°Π»ΡΠ½ΡΠΌΠΈ ΡΡΠ°Π΅ΠΊΡΠΎΡΠΈΡΠΌΠΈ ΡΠ»Π΅ΠΌΠ΅Π½ΡΠΎΠ², Π° ΠΎΠ½ΠΈ β Ρ Π²Π»ΠΈΡΠ½ΠΈΠ΅ΠΌ Π³ΡΠ°Π½ΠΈΡ, ΡΡΠΎ Π΄Π°Π΅Ρ Π²Π΅ΡΠ²Π»Π΅Π½ΠΈΠ΅ ΠΏΠΎΡΡΠ΄ΠΊΠ° ΠΌΠ°ΡΡΠΈΡΡ ΠΏΠΎ ΠΊΠ»Π°ΡΡΡ Π²ΡΡΠ΅ΡΠ° ΠΏΠΎ ΠΌΠΎΠ΄ΡΠ»Ρ 3,4 ΠΈΠ»ΠΈ 12. ΠΠΈΠ·ΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΡΡΠΈΡ
ΡΡΠ°Π΅ΠΊΡΠΎΡΠΈΠΉ ΠΏΡΠΈΠ²ΠΎΠ΄ΡΡΡΡ Π² ΡΠ°ΡΡΠΈΡΠ΅Π½Π½ΠΎΠΌ ΠΏΠΎΠ»Π΅ ΠΠ. Π ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠ° Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠΈ ΠΠ Π°Π½Π°Π»ΠΈΠ·ΠΈΡΡΡΡΡΡ Π΄Π²Π΅ Β«ΠΌΠ΅ΡΡΠΈΠΊΠΈ ΠΏΠ΅ΡΠ΅ΠΌΠ΅ΡΠ°Π½Π½ΠΎΡΡΠΈΒ» Π½Π° ΠΏΠ΅ΡΠΌΡΡΠ°ΡΠΈΡΡ
ΠΌΠ°ΡΡΠΈΡΡ (ΠΏΠΎ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ Ρ Π½Π°ΡΠ°Π»ΡΠ½ΠΎΠΉ). ΠΠ»Ρ Π²ΡΠ΅Ρ
ΡΡ
Π΅ΠΌ ΠΈ Π±ΠΎΠ»ΡΡΠΈΠ½ΡΡΠ²Π° Π²Π΅ΡΠ²Π΅ΠΉ ΠΏΠΎΠ²Π΅Π΄Π΅Π½ΠΈΠ΅ ΡΡΠΈΡ
ΠΌΠ΅ΡΡΠΈΠΊ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΎ Π½Π° Π³ΡΠ°ΡΠΈΠΊΠ°Ρ
ΠΈ Π³ΠΈΡΡΠΎΠ³ΡΠ°ΠΌΠΌΠ°Ρ
(ΡΡΠ»ΠΎΠ²Π½ΠΎ: ΠΏΠ»ΠΎΡΠ½ΠΎΡΡΠΈ ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ), ΠΏΠΎΠΊΠ°Π·ΡΠ²Π°ΡΡΠΈΡ
, ΠΊΠ°ΠΊ ΡΠ°ΡΡΠΎ Π²ΡΡΡΠ΅ΡΠ°ΡΡΡΡ ΠΏΠΎ ΠΏΠ΅ΡΠΈΠΎΠ΄Ρ ΠΏΠ΅ΡΠΌΡΡΠ°ΡΠΈΠΈ Ρ Π·Π°Π΄Π°Π½Π½ΡΠΌ ΠΈΠ½ΡΠ΅ΡΠ²Π°Π»ΠΎΠΌ Π·Π½Π°ΡΠ΅Π½ΠΈΠΉ ΠΌΠ΅ΡΡΠΈΠΊ. ΠΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΎΠ΅ Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅ ΡΠ°Π±ΠΎΡΡ ΡΠΎΡΡΠΎΠΈΡ Π² ΠΎΡΠ΅Π½ΠΊΠ΅ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΠ Π² ΠΎΠ±Π»Π°ΡΡΡΡ
Π³Π΅Π½Π΅ΡΠ°ΡΠΈΠΈ ΠΏΡΠ΅Π²Π΄ΠΎΡΠ»ΡΡΠ°ΠΉΠ½ΡΡ
ΡΠΈΡΠ΅Π» ΠΈ ΠΊΡΠΈΠΏΡΠΎΠ³ΡΠ°ΡΠΈΠΈ
Differences between familial and sporadic dilated cardiomyopathy: ESC EORP Cardiomyopathy & Myocarditis registry
Aims:
Dilated cardiomyopathy (DCM) is a complex disease where genetics interplay with extrinsic factors. This study aims to compare the phenotype, management, and outcome of familial DCM (FDCM) and nonβfamilial (sporadic) DCM (SDCM) across Europe.
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Methods and results:
Patients with DCM that were enrolled in the prospective ESC EORP Cardiomyopathy & Myocarditis Registry were included. Baseline characteristics, genetic testing, genetic yield, and outcome were analysed comparing FDCM and SDCM; 1260 adult patients were studied (238 FDCM, 707 SDCM, and 315 not disclosed). Patients with FDCM were younger (P < 0.01), had less severe disease phenotype at presentation (P < 0.02), more favourable baseline cardiovascular risk profiles (P β€ 0.007), and less medication use (P β€ 0.042). Outcome at 1 year was similar and predicted by NYHA class (HR 0.45; 95% CI [0.25β0.81]) and LVEF per % decrease (HR 1.05; 95% CI [1.02β1.08]. Throughout Europe, patients with FDCM received more genetic testing (47% vs. 8%, P < 0.01) and had higher genetic yield (55% vs. 22%, P < 0.01).
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Conclusions:
We observed that FDCM and SDCM have significant differences at baseline but similar shortβterm prognosis. Whether modification of associated cardiovascular risk factors provide opportunities for treatment remains to be investigated. Our results also show a prevalent role of genetics in FDCM and a nonβmarginal yield in SDCM although genetic testing is largely neglected in SDCM. Limited genetic testing and heterogeneity in panels provides a scaffold for improvement of guideline adherence
Blood pressure and metabolic effects of acetyl-L-carnitine in type 2 diabetes: DIABASI randomized controlled trial
Context: Acetyl-L-carnitine (ALC), a mitochondrial carrier involved in lipid oxidation and glucose metabolism, decreased systolic blood pressure (SBP), and ameliorated insulin sensitivity in hypertensive nondiabetic subjects at high cardiovascular risk. Objective: To assess the effects of ALC on SBP and glycemic and lipid control in patients with hypertension, type 2 diabetes mellitus (T2D), and dyslipidemia on background statin therapy. Design: After 4-week run-in period and stratification according to previous statin therapy, patients were randomized to 6-month, double-blind treatment with ALC or placebo added-on simvastatin. Setting: Five diabetology units and one clinical research center in Italy. Patients: Two hundred twenty-nine patients with hypertension and dyslipidemic T2D > 40 years with stable background antihypertensive, hypoglycemic, and statin therapy and serum creatinine < 1.5 mg/ dL. Interventions: Oral ALC 1000 mg or placebo twice daily on top of stable simvastatin therapy. Outcome and Measures: Primary outcome was SBP. Secondary outcomes included lipid and glycemic profiles. Total-body glucose disposal rate and glomerular filtration rate were measured in subgroups by hyperinsulinemic-euglycemic clamp and iohexol plasma clearance, respectively. Results: SBP did not significantly change after 6-month treatment with ALC compared with placebo (-2.09mmHg vs-3.57mmHg, P = 0.9539). Serum cholesterol, triglycerides, and lipoprotein(a), as well as blood glucose, glycated hemoglobin, fasting insulin levels, homeostatic model assessment of insulin resistance index, glucose disposal rate, and glomerular filtration rate did not significantly differ between treatments. Adverse events were comparable between groups. Conclusions: Six-month oral ALC supplementation did not affect blood pressure, lipid and glycemic control, insulin sensitivity and kidney function in hypertensive normoalbuminuric and microalbuminuric T2D patients on background statin therapy
Cardiopoietic cell therapy for advanced ischemic heart failure: results at 39 weeks of the prospective, randomized, double blind, sham-controlled CHART-1 clinical trial
Cardiopoietic cells, produced through cardiogenic conditioning of patients' mesenchymal stem cells, have shown preliminary efficacy. The Congestive Heart Failure Cardiopoietic Regenerative Therapy (CHART-1) trial aimed to validate cardiopoiesis-based biotherapy in a larger heart failure cohort
Four Cellular Automata Algorithms for Matrix Permutation
Numerical calculation uses to describe the operation of matrix permutation algorithms based on cyclic shifts of rows and columns. This choice of discrete transformation algorithms justified by the convenience of the cellular automaton (CA) formulation, which is used. Obtained Empirical formulas for the permutation period and for the last algorithm, which period formula is recurrent. For a base scheme period has the asymptotics: Β for a matrix Β with pairwise different elements. Despite the complexity of the scheme, the other two modifications only give a polynomial growth of period, no higher than 3. Fourth scheme has a non-trivial period dependence, but no higher than the exponential. In some cases algorithms make special permutations: rotate, reflect, and rearrange blocks for the matrix . These formulas are closely related to individual cells paths. And paths connected with the influence of the boundaries that gives branching the matrix order by subtraction class modulo 3,4 or 12. Visualizations of these paths make in the extended CA-field. Two "mixing metrics" analyze as a parameter of CA dynamics on matrix permutations (compared to the initial). For all schemes and most branches, the behavior of these metrics shows in graphs and histograms (conditional density distribution) showing how often the permutation period occurs with the specified interval of metrics. The practical aim of this work is in the field of pseudorandom number generation and cryptography
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