Correction to: Semi-automation of process analytics reduces operator effect.

In the original publication, open access funding institution was incorrectly mentioned in the acknowledgement section.

Semi-automation of process analytics reduces operator effect.

The aim of this study was to semi-automate process analytics for the quantification of common impurities in downstream processing such as host cell DNA, host cell proteins and endotoxins using a commercial liquid handling station. By semi-automation, the work load to fully analyze the elution peak of a purification run was reduced by at least 2.41 h. The relative standard deviation of results among different operators over a time span of up to 6 months was at the best reduced by half, e.g. from 13.7 to 7.1% in dsDNA analysis. Automation did not improve the reproducibility of results produced by one operator but released time for data evaluation and interpretation or planning of experiments. Overall, semi-automation of process analytics reduced operator-specific influence on test results. Such robust and reproducible analytics is fundamental to establish process analytical technology and get downstream processing ready for Quality by Design approaches.

Uric acid and obesity-related phenotypes in postmenopausal women.

The aim of this study was to find the genetic, metabolic, and nutritional risk factors, which can be associated with uric acid (UA) level. The risk factors related to uricemia were assessed among 271 postmenopausal women without cardiometabolic disorders and hypolipidemic/hypoglycemic treatment selected from a cohort of 1423 obese postmenopausal women. The bioimpedance analysis and biochemical and genetic analyses were performed in two groups characterized by serum UA ≥ 4 mg/dL (238 µmol/L) and < 4 mg/dL. The TaqMan-based real-time PCR method was applied to assess the role of Pro12Ala of peroxisome proliferation-activated receptor (PPAR)gamma-2 and Trp64Arg of beta-3-adrenergic receptor (ADRB) polymorphisms. Women with UA level ≥ 4 mg/dL were characterized by larger body mass, triceps skinfold, waist circumference, body fat amount, and serum insulin, glucose, and triglyceride levels. There was no difference in dietary habits between the analyzed groups. Body mass, waist circumference, body fat amount, diastolic blood pressure, and serum insulin, glucose, high-density lipoprotein, and triglyceride levels, Homeostasis Model Assessment-Insulin Resistance, and energy from the dietary fat influence the UA level ≥ 4 mg/dL; however, the serum UA was not determined by Pro12Ala and Trp64Arg polymorphism analyses. The model of linear regression revealed that the group characterized by body mass index  ≥ 25 kg/m2 and glucose ≥ 100 mg/dL has 4 times increased risk of UA level (p = 0.0009); after adding triglycerides ≥ 150 mg/dL, the risk of UA increased 7 times (p = 0.0216). Increasing the level of UA ≥ 4 mg/dL is associated with overweight, hyperglycemia, and hypertriglyceridemia in women without a history of cardiometabolic disorders. A better management of metabolic factors could help prevent further increase in UA levels.

The Gene-Diet Associations in Postmenopausal Women with Newly Diagnosed Dyslipidemia.

OBJECTIVES: The aim of this study was to determine the relationship between polymorphisms of peroxisome proliferator activated receptor - PPAR gamma-2 (Pro12Ala, C1431T) and beta 3-adrenergic receptor - ADRB3 (Trp64Arg) and dietary habits in a group of postmenopausal women who were not under hypolipidemic treatment. DESIGN: Genetic, nutritional and anthropometric parameters were measured in 213 dyslipidemic (LDL ≥115 mg/dL) and 58 normolipidemic (LDL<115) postmenopausal women. The PCR-RFLP method were used to determine the distributions of selected alleles and genotype frequencies. Dietary intake of basic components and fatty acids was obtained from a 7-day weighed food record and the bio-impedance method was used to determine nutritional status. RESULTS: Nearly 79% of analyzed women were in the first-time-diagnosed dyslipidemic state. The dyslipidemic subjects were characterized with higher intake of energy, fat, and saturated fatty acids (SFA). The analysis of the same polymorphisms showed association at the P value <0.05 with nutrients (fat, SFA, and polyunsaturated fatty acid - PUFA and saccharose) and elevated LDL level. Higher PUFA intake in a group of women with the protective Ala12/X polymorphism did not increase the risk of dyslipidemia even though they were characterized by visceral distribution of fat. The Arg64/X polymorphism and higher intake of energy, fat, and arachidic acid intake (C20:0) were associated with dyslipidemic state. CONCLUSION: Both nutritional and genetic factors are related to lipid profile. The identification of gene-diet associations is likely to provide useful information about the etiology of postmenopausal dyslipidemia and help in effective treatment.