Leishmania tarentolae (LEXSY) system is an inexpensive and effective expression approach for various research and medical purposes. The stated advantages of this system are the possibility of obtaining the soluble product in the cytoplasm, a high probability of correct protein folding with a full range of post-translational modifications (including uniform glycosylation), and the possibility of expressing multi-subunit proteins. In this paper, a LEXSY expression system has been employed for obtaining the receptor binding domain (RBD) of the spike-protein of the SARS-CoV-2 virus and the homopentameric acetylcholine-binding protein (AChBP) from Lymnaea stagnalis. RBD is actively used to obtain antibodies against the virus and in various scientific studies on the molecular mechanisms of the interaction of the virus with host cell targets. AChBP represents an excellent structural model of the ligand-binding extracellular domain of all subtypes of nicotinic acetylcholine receptors (nAChRs). Both products were obtained in a soluble glycosylated form, and their structural and functional characteristics were compared with those previously described.

Chronological age can be estimated from molecular profiles; however, predictive accuracy alone does not establish a biologically validated ageing clock. We therefore evaluated whether plasma protein intensities measured by data-independent acquisition mass spectrometry (DIA-MS) support chronological-age estimation in a cross-sectional cohort from Moscow, Russia, while explicitly examining acquisition-order and MS-batch confounding. Plasma samples from 350 adult participants aged 19.8 to 99.1 years were analyzed as single-shot, single-injection DIA-MS acquisitions on a Q Exactive HFX platform. The final matrix comprised 222 protein groups and was generated with DIA-NN version 1.8.1 using a two-pass predicted-library-assisted workflow, cross-run precursor-level normalization, normalized MaxLFQ-like protein–group quantification, and 1% precursor and protein–group false-discovery-rate thresholds. Descriptive associations were assessed using Spearman rank correlation with Bonferroni correction. Feature selection and missing-value imputation were confined to each training subset. Generalization was assessed primarily by holding out complete MS batches, with repeated, nested, and random five-fold analyses retained as internal comparators. Of the 222 proteins, 148 were associated with chronological age at the nominal threshold p < 0.05, and 98 remained significant after Bonferroni correction. The strongest descriptive associations involved fibrinogen beta chain (FGB; rs = 0.590), SERPINA3 (rs = 0.560), ITIH4 (rs = 0.542), complement C5 (rs = 0.538), and ceruloplasmin (rs = 0.518). In internal random-split analyses, a 50-feature Gradient Boosting strategy yielded MAE = 8.90 ± 1.06 years in repeated five-fold cross-validation and MAE = 9.10 ± 1.10 years in nested cross-validation. However, when complete MS batches were held out, aggregate MAE increased to 10.56 years (R² = 0.554) using a batch-stratified assignment and 11.87 years (R² = 0.461) using GroupKFold. These batch-held-out results constitute the primary estimate of generalization from the present dataset. To assess the trade-off between error and assay complexity, strategies using 5, 8, 10, 12, 15, 20, 30, and 50 proteins selected within each training fold were compared. The 8-feature strategy yielded MAE = 11.83 years in repeated random cross-validation, whereas the 50-feature strategy yielded MAE = 8.90 years. These estimates concern feature-number strategies, not fixed panels. The post hoc eight-protein list is reported only as an exploratory candidate set for future independent validation. Acquisition order was associated with chronological age (rs = 0.336, p = 1.03 × 10⁻¹⁰), and age and sex distributions differed strongly across 27 MS batches. Order alone predicted age with MAE = 3.98 years in a negative-control model, demonstrating that age information was encoded in the acquisition design. Accordingly, the present findings support only a proof-of-concept for plasma-proteomic prediction of chronological age within this dataset; they do not establish a batch-independent model, a clinical tool, or a biologically validated ageing clock.

Glioblastoma multiforme (GBM) is the most aggressive primary brain tumor, characterized by an extremely poor prognosis. Difficulties in diagnostics and monitoring this disease stimulate the search for minimally invasive approaches. In this context liquid biopsy is considered as a promising approach. This review analyzes results of recent studies aimed at identifying circulating protein biomarkers of GBM in plasma and serum. These biomarkers include cell-free circulating plasma proteins and proteins of extracellular vesicles (EVs). Special attention is paid to the results obtained using both immunochemical methods and mass spectrometric approaches for identification of protein biomarkers, which have been summarized here as a list of identified potential diagnostic and prognostic biomarkers. Analysis of the literature demonstrates that proteomic analysis focused on the plasma EV fraction significantly expands the possibilities for identifying biomarkers for noninvasive GBM diagnostics and monitoring.