A method for the determination of amizone and amixin in the same sample and in their mixture with antibiotics (ceftriaxone, tetracycline, ampicillin and levofloxacin) was developed using reversed-phase highperformance liquid chromatography equipped with a photodiode array detector. A SunFire C18 column, a mobile phase consisting of sodium perchlorate buffer (pH = 2.5) and acetonitrile (75:25), at a flow rate of 0.8 ml/min, was used. The analytes were identified at 205 and 265 nm. The specificity, linearity, precision parameters, LOD and LOQ were evaluated during the validation of the methodology, and the correlation coefficients of amizone and amixin were 0.9992 and 0.9998, respectively. This method can also be used for the determination of amizone and amixin and their presence in a mixture with antibiotics in the environment.
Cardiovascular diseases are the leading cause of morbidity and mortality in adults worldwide. There is one common pathophysiological aspect present in all cardiovascular diseases—dysfunctional heart rhythm regulation. Taking this aspect into consideration for cardiovascular risk predictions opens important research perspectives, allowing for the development of preventive treatment techniques. The aim of this study was to find out whether certain pathologically appearing signs in the heart rate variability (HRV) of an apparently healthy person, even with high HRV, can be defined as biomarkers for a disturbed cardiac regulation and whether this can be treated preventively by a drug-free method. This multi-phase study included 218 healthy subjects of either sex, who consecutively visited the physician at Gesundheit clinic because of arterial hypertension, depression, headache, psycho-emotional stress, extreme weakness, disturbed night sleep, heart palpitations, or chest pain. In study phase A, baseline measurement to identify individuals with cardiovascular risks was done. Therefore, standard HRV, as well as the new cardiorhythmogram (CRG) method, were applied to all subjects. The new CRG analysis used here is based on the recently introduced LF drops and HF counter-regulation. Regarding the mechanisms of why these appear in a steady-state cardiorhythmmogram, they represent non-linear event-based dynamical HRV biomarkers. The next phase of the study, phase B, tested whether the pathologically appearing signs identified via CRG in phase A could be clinically influenced by drug-free treatment. In order to validate the new CRG method, it was supported by non-linear HRV analysis in both phase A and in phase B. Out of 218 subjects, the pathologically appearing signs could be detected in 130 cases (60%), p < 0.01, by the new CRG method, and by the standard HRV analysis in 40 cases (18%), p < 0.05. Thus, the CRG method was able to detect 42% more cases with pathologically appearing cardiac regulation. In addition, the comparative CRG analysis before and after treatment showed that the pathologically appearing signs could be clinically influenced without the use of medication. After treatment, the risk group decreased eight-fold—from 130 people to 16 (p < 0.01). Therefore, progression of the detected pathological signs to structural cardiac pathology or arrhythmia could be prevented in most of the cases. However, in the remaining risk group of 16 apparently healthy subjects, 8 people died due to all-cause mortality. In contrast, no other subject in this study has died so far. The non-linear parameter which is able to quantify the changes in CRGs before versus after treatment is FWRENYI4 (symbolic dynamic feature); it decreased from 2.85 to 2.53 (p < 0.001). In summary, signs of pathological cardiac regulation can be identified by the CRG analysis of apparently healthy subjects in the early stages of development of cardiac pathology. Thus, our method offers a sensitive biomarker for cardiovascular risks. The latter can be influenced by non-drug treatments (acupuncture) to stop the progression into structural cardiac pathologies or arrhythmias in most but not all of the patients. Therefore, this could be a real and easy-to-use supplemental method, contributing to primary prevention in cardiology.
Background. Obesity results from a malfunction of the body's weight-control mechanisms, which may be influenced by environmental changes. Essentially, the obesity risk relies on two significant interdependent factors: genetic variations (single-nucleotide polymorphisms, haplotypes) and environmental risk exposure. Due to new biotechnologies over 127 potential genes for obesity have been identified, and evidence supports the function of 22 genes in at least five distinct groups. Gene and environment interactions mean that the synergy between genotype and environment is neither additive or multiplicative. The application of innovative methods for both genotype and lifestyle variables should be emphasized. Aim of study: Investigate variable data of lifestyle factors in obese people with genetic predisposition and without in order to figure out the trigger risks which transform the predisposition into obesity. Material and methods: This is a descriptive study. A questionnaire was elaborated. It was developed based on the data of new biotechnological analysis of metabolic changes in obese humans. 142 individuals were included. 82 obese individuals, 42 with genetic predisposition and 40 without, and 60 healthy probands were interviewed. Further followed a comparative statistical analysis. Results: Obese probands were found with higher levels of disability compare those without, cardiovascular events higher compared with healthy probnads, disability level and smoking habits had significantly correlation in obese with genetic predisposition. On the other hand, health probands were found in higher level of anxiety compared obese people with genetic predisposition. Conclusions: All the lifestyle aspects which lead to an increased central nervous overactivity disturb significantly the metabolism and are critical risk factors for people with genetic predisposition relate to the pathogenesis of obesity. that might lead to high disability level associated comorbid states and high risk of cardiovascular events.
The most prevalent persistent arrhythmia in cardiology is atrial fibrillation. Former atrial fibrillation which appears without any underlying reason was called „lone atrial fibrillation“. Due to new biotechnological methods in electrophysiology, like mapping, unusual conducting mechanisms were stabilized. Due to new biotechnological methods of DNA analysis recently the reason is detected. This is a genetically determined atrial fibrillation. The aim of this study is to analyse what are the most common mutations which lead to atrial fibrillation. Material and methods. This is a systematic review study. The sources of information which were analysed are mostly from google scholar and web of science. From 2000 sources, several sources were filtered out by the keywords and remained 14 sources on which is based this review study. Results. More than 70 genes are recently detected which lead to atrial fibrillations. Majority of them are mutations of the genes which encode the transport proteins of the heart’s conductive system. The most common mutations that lead to genetically determined atrial fibrillation occure in KCNQ1, KCNA5 and 6q14–16. Conclusions. Before starting treatment of lone atrial fibrillation, a genetical test should be done in order to stabilize the type of the underlying mutation. This is a tactical step in taking the decision on treatment strategy by antiarrhytmic drugs or ablation. So ablatogenoics is the best solution for patients with genetically determined atrial fibrillation.
Background. Regarding the high incidence of cardiovascular diseases, it is critical to find predictors. The aim of this study is to appreciate the predivtive value of of recently-found parameters of cardiorhythmogram analysis applying the new biophysical approach for predicting the recurrence of atrial fibrillation. Material and methods. This is a case-series study, where 350 cardiorhythmograms were assessed. For assessment both methods were applied, the standard heart rate variability analysis and new approach by the parameters HF counterregulation and LF drops. Results. The both newly-found parameters predict reliably atrial fibrillation recurrence. The significance of the parameter HF counterregulation is p < 0.0001, in case of the parameter LF drops it is p < 0.001. Conclusions. In case if prediction is needed, the standard heart rate variability should be completed by the new biophysical approach, applying the parameters HF counterregulation and LF drops. Steady-state cardiorhythmograms with events of unstationarity can be realiably analysed just by these parameters. Events of unstationarity are informative sources for prediction.
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