Abstract. The aim of the present study was to determine the association between sirtuin 1 (SIRT1), fibroblast growth factor receptor 2 (FGFR2) and signal transducer and activator of transcription 3 (STAT3) polymorphisms, and pituitary adenoma (PA) development, invasiveness, hormonal activity and recurrence. The present study included 143 patients with a diagnosis of PA. The reference group involved 808 healthy subjects. The genotyping of SIRT1 rs12778366, FGFR2 rs2981582 and STAT3 rs744166 was performed using the quantitative polymerase chain reaction method. The SIRT1 rs12778366 polymorphism analysis in the overall group revealed differences in the genotype distribution between patients with PA and control group subjects. The rs12778366 T/C genotype was observed to be different in non-invasive, non-recurrent and inactive PA subgroups compared with the control group, while the C/C genotype was observed to be different in invasive, recurrent and active PA subgroups compared with the control group. STAT3 rs744166 polymorphism analysis in the overall group revealed differences in the genotype distribution between patients with PA and the control groups. The rs744166 G/G genotype was observed to be different in invasive, non-recurrent and active PA subgroups compared with the control group, while the rs744166 A/A genotype was observed to be different in the active PA subgroup compared with the control group, and was also different in terms of invasiveness and recurrence in PA subgroups. The present study demonstrated that SIRT1 rs12778366 is associated with pituitary adenoma development while STAT3 rs744166 is associated with PA invasiveness, hormonal activity and recurrence.

Artificial intelligence (AI)-based diagnostic algorithms have achieved ambitious aims through automated image pattern recognition. For neurological disorders, this includes neurodegeneration and inflammation. Scalable imaging technology for big data in neurology is optical coherence tomography (OCT). We highlight that OCT changes observed in the retina, as a window to the brain, are small, requiring rigorous quality control pipelines. There are existing tools for this purpose. Firstly, there are human-led validated consensus quality control criteria (OSCAR-IB) for OCT. Secondly, these criteria are embedded into OCT reporting guidelines (APOSTEL). The use of the described annotation of failed OCT scans advances machine learning. This is illustrated through the present review of the advantages and disadvantages of AI-based applications to OCT data. The neurological conditions reviewed here for the use of big data include Alzheimer disease, stroke, multiple sclerosis (MS), Parkinson disease, and epilepsy. It is noted that while big data is relevant for AI, ownership is complex. For this reason, we also reached out to involve representatives from patient organizations and the public domain in addition to clinical and research centers. The evidence reviewed can be grouped in a five-point expansion of the OSCAR-IB criteria to embrace AI (OSCAR-AI). The review concludes by specific recommendations on how this can be achieved practically and in compliance with existing guidelines.

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Background and Objective. The aim of this study was to evaluate associations between visual functions (visual acuity, perimetry, optic nerve disc condition, and color contrast sensitivity) and pituitary adenoma (PA) diameter. Material and Methods. In the study, 20 patients with PA, which was confirmed by computed tomography or magnetic resonance imaging scans, were examined. The patients were divided into 2 groups: those with a PA diameter of ≤1 cm (14 eyes) and with a PA diameter of >1 cm (26 eyes). The control group comprised 40 healthy age- and gender-matched persons (80 eyes). The diameter of PA, visual acuity, and perimetry were analyzed; the F-M 100 hue test for color discrimination was used in patients with PA. Results. Visual acuity was better in the control group as compared with both groups of patients (1.0 vs. 0.90 [SD, 0.50] and 0.64 [SD, 0.21]; P=0.01; respectively). The results of the Farnsworth- Munsell 100 hue test were also better in the control group compared with the patients with PA of ≤1 cm and >1 cm (error score of 80.1 [SD, 53.0] vs. 131.8 [SD, 30.6] and 244.68 [SD, 51. 6], respectively; P=0.011). There was a very strong positive correlation between the error score of the F-M 100 hue test and PA diameter (r=0.905), but the correlation between the error score and visual acuity (r=–0.32), perimetry (r=0.21), and eye fundus changes (r=0.36) and PA diameter was weak. Conclusions. Our results showed that PA can cause the impairments of visual acuity, perimetry, and color contrast sensitivity. The computerized F-M 100 hue test can be one of the methods for an early diagnosis of chiasm damage in patients with PA.

The aim of the present study was to determine if the Ki-67 labelling index reflects invasiveness of pituitary adenoma and to evaluate IL-17A concentration in blood serum of pituitary adenoma patients. The study was conducted in the Hospital of Lithuanian University of Health Sciences. All pituitary adenomas were analysed based on magnetic resonance imaging findings. The suprasellar extension and sphenoid sinus invasion by pituitary adenoma were classified according to Hardy classification modified by Wilson. Knosp classification system was used to quantify the invasion of the cavernous sinus. The Ki-67 labelling index was obtained by immunohistochemical analysis with the monoclonal antibody, and serum levels of IL-17A were determined by enzyme-linked immunosorbent assay (ELISA). Sixty-nine PA tissue samples were investigated. Serum levels of IL–17A were determined in 60 patients with PA and 64 control subjects. Analysis revealed statistically significantly higher Ki-67 labelling index in invasive compared to noninvasive pituitary adenomas. Median serum IL-17A level was higher in the pituitary adenoma patients than in the control group. Conclusion. IL-17A might be a significant marker for patients with pituitary adenoma and Ki-67 labelling index in case of invasive pituitary adenomas.