Micro-RNAs miR-375-3p and miR-7-5p are released alongside ACTH from corticotroph pituitary neuroendocrine tumor

Objective: Circulating miRNAs are found in bodily fluids including plasma and can serve as biomarkers for diseases. The aim of this study was to provide the first insight into the landscape of circulating miRNAs in close proximity to the adrenocorticotropic hormone (ACTH) secreting PitNET. To achieve this objective next-generation sequencing of miRNAs in plasma from bilateral inferior petrosal sinus sampling (BIPSS) - a gold standard in diagnosing ACTH-secreting PitNETs, was carried out. Methods: Sinistral (left) and dextral (right) BIPSS blood samples of the patient were collected in three time points: before the administration of corticotropin-releasing hormone, 5 and 15 minutes after stimulation. Peripheral venous blood samples were also collected 24 hours before and after BIPSS and before the resection of PitNET and 24 hours after. In differential expression analysis sinistral plasma was compared with dextral. Results: BIPSS concluded that the highest amount of ACTH was released in the sinistral side at the 5th minute mark indicating a presence of tumor. The highest amount of differentially expressed miRNAs was observed 5 minutes after stimulation (20 upregulated, 14 downregulated). At the 5th minute mark in sinistral plasma, two miRNAs were identified: hsa-miR-7-5p and hsa-miR-375-3p that were highly upregulated compared to other BIPSS samples and peripheral plasma samples. Clustering analysis showed that BIPSS plasma differs from peripheral plasma in miRNA expression patterns. Conclusions: data indicates that ACTH-secreting PitNET actively releases two circulating miRNAs upon stimulation with CRH (hsa-mir-7-5p, hsa-mir-375-3p) alongside with ACTH implying further studies of these miRNA as diagnostic markers are needed.

AN APPLICATION OF MICROWAVES REFRACTION FOR INHOMOGENEOUS PLASMA DIAGNOSTIC

A brief review of the main microwave diagnostics methods of inhomogeneous plasma based on the refraction of microwaves is given. These methods make it possible to determine the plasma density distribution, the magnetic field distribution, the electron collision frequency, and the electron temperature profile. In addition, the determination of the average density of the peripheral plasma layers and the local inhomogeneities of the rotating plasma are also possible. The effect of refraction on the accuracy of determining the plasma parameters by using microwave methods for plasma diagnostics is considered.

Plasma d-glutamate levels for detecting mild cognitive impairment and Alzheimer’s disease: Machine learning approaches

Background: d-glutamate, which is involved in N-methyl-d-aspartate receptor modulation, may be associated with cognitive ageing. Aims: This study aimed to use peripheral plasma d-glutamate levels to differentiate patients with mild cognitive impairment (MCI) and Alzheimer’s disease (AD) from healthy individuals and to evaluate its prediction ability using machine learning. Methods: Overall, 31 healthy controls, 21 patients with MCI and 133 patients with AD were recruited. Serum d-glutamate levels were measured using high-performance liquid chromatography (HPLC). Cognitive deficit severity was assessed using the Clinical Dementia Rating scale and the Mini-Mental Status Examination (MMSE). We employed four machine learning algorithms (support vector machine, logistic regression, random forest and naïve Bayes) to build an optimal predictive model to distinguish patients with MCI or AD from healthy controls. Results: The MCI and AD groups had lower plasma d-glutamate levels (1097.79 ± 283.99 and 785.10 ± 720.06 ng/mL, respectively) compared to healthy controls (1620.08 ± 548.80 ng/mL). The naïve Bayes model and random forest model appeared to be the best models for determining MCI and AD susceptibility, respectively (area under the receiver operating characteristic curve: 0.8207 and 0.7900; sensitivity: 0.8438 and 0.6997; and specificity: 0.8158 and 0.9188, respectively). The total MMSE score was positively correlated with d-glutamate levels ( r = 0.368, p < 0.001). Multivariate regression analysis indicated that d-glutamate levels were significantly associated with the total MMSE score ( B = 0.003, 95% confidence interval 0.002–0.005, p < 0.001). Conclusions: Peripheral plasma d-glutamate levels were associated with cognitive impairment and may therefore be a suitable peripheral biomarker for detecting MCI and AD. Rapid and cost-effective HPLC for biomarkers and machine learning algorithms may assist physicians in diagnosing MCI and AD in outpatient clinics.

Сравнение вариантов литиевого дивертора для токамака ДЕМО-ТИН

For the DEMO-FNS tokamak being developed in Russia, the choice of the divertor concept with evaporating liquid lithium is discussed, which meets the requirements for removing the heat load from the peripheral plasma and provides an acceptable level of change in the ionic composition of the core plasma. The paper presents the results of numerical modeling and optimization of divertor parameters with several chambers partitioned by slotted nozzles. The parameters of lithium fluxes flowing into the peripheral layer are estimated for the temperature range of the divertor chambers from 500 to 1000 K under gas-kinetic and molecular flow regimes of lithium vapor from the divertor. The fulfilled analysis of processes that reduce the outflow of lithium from the chambers and its penetration into the core plasma volume inside the separatrix showed that sectioning effectively reduces the Li fluxes to acceptable levels of ~ 1020 atoms per second.