PATHOGENETIC ROLE OF MACROPHAGE COLONY-STIMULATING FACTOR (CSF-1) IN PREDICTING ENDOMETRIOID DISEASE.

OBJECTIVE: The aim: To assess the CSF - 1 level in peritoneal fluid and menstrual blood of women with endometrioid disease and to investigate its diagnostic and prognostic specificity. PATIENTS AND METHODS: Materials and methods: The study included 80 women of child-bearing age (mean age 30.95 ± 6.49 years) with benign gynaecological pathology of the ovaries and / or fallopian tubes. The women included in the study were divided into two groups: study group (n = 50, mean age 31.04 ± 6.3 years), consisting of patients with confirmed endometrioid disease, and control group (n = 30, mean age 30.8 ± 6.8 years), involving individuals without signs of endometriosis (p> 0.05). RESULTS: Results: We have found significantly higher level of CSF-1 content in the peritoneal fluid in the subjects of the study group (2027.05 ± 732.64 pg / ml) compared with those in the control group (1725.62 ± 466.06 pg / ml) (p = 0.029). There is a tendency towards an increase in CSF-1 level in women with endometriosis in its more severe stages and more severe and extended adhesions. The investigation of CSF-1 content in menstrual blood has demonstrated significant increase in its values in the women of the study group (9431.6 ± 2866.22 pg / ml) compared with the values in the control group (6637.12 ± 954.05 pg / ml), (p = 0.00004). Thus, there is a tendency towards the growth in CSF-1 level in peritoneal fluid and menstrual blood in women with endometriosis and concurrent increase in severity of the disease. CONCLUSION: Conclusions: There has been found significant increase in CSF-1 content in women with endometrioid disease in both peritoneal fluid and menstrual blood (1.2 and 1.4 times, respectively). Thus, macrophage growth factor (CSF-1) can be used as a diagnostic and prognostic criterion in evaluating the progression of endomertioid disease.

Generative Algorithm for Molecular Graphs Uncovers Products of Oil Oxidation.

The autoxidation of triglyceride (or triacylglycerol, TAG) is a poorly understood complex system. It is known from mass spectrometry measurements that, although initiated by a single molecule, this system involves an abundance of intermediate species and a complex network of reactions. For this reason, the attribution of the mass peaks to exact molecular structures is difficult without additional information about the system. We provide such information using a graph theory-based algorithm. Our algorithm performs an automatic discovery of the chemical reaction network that is responsible for the complexity of the mass spectra in drying oils. This knowledge is then applied to match experimentally measured mass spectra with computationally predicted molecular graphs. We demonstrate this methodology on the autoxidation of triolein as measured by electrospray ionization-mass spectrometry (ESI-MS). Our protocol can be readily applied to investigate other oils and their mixtures.