Diabetic nephropathy, hypertension, and glomerulonephritis are the most common causes of chronic kidney diseases (CKD). Since CKD of various origins may not become apparent until kidney function is significantly impaired, a differential diagnosis and an appropriate treatment are needed at the very early stages. Conventional biomarkers may not have sufficient separation capabilities, while a full-proteomic approach may be used for these purposes. In the current study, several machine learning algorithms were examined for the differential diagnosis of CKD of three origins. The tested dataset was based on whole proteomic data obtained after the mass spectrometric analysis of plasma and urine samples of 34 CKD patients and the use of label-free quantification approach. The k-nearest-neighbors algorithm showed the possibility of separation of a healthy group from renal patients in general by proteomics data of plasma with high confidence (97.8%). This algorithm has also be proven to be the best of the three tested for distinguishing the groups of patients with diabetic nephropathy and glomerulonephritis according to proteomics data of plasma (96.3% of correct decisions). The group of hypertensive nephropathy could not be reliably separated according to plasma data, whereas analysis of entire proteomics data of urine did not allow differentiating the three diseases. Nevertheless, the group of hypertensive nephropathy was reliably separated from all other renal patients using the k-nearest-neighbors classifier “one against all” with 100% of accuracy by urine proteome data. The tested algorithms show good abilities to differentiate the various groups across proteomic data sets, which may help to avoid invasive intervention for the verification of the glomerulonephritis subtypes, as well as to differentiate hypertensive and diabetic nephropathy in the early stages based not on individual biomarkers, but on the whole proteomic composition of urine and blood.

We selected DNA aptamers to the epithelial cell adhesion molecule (EpCAM) expressed on primary lung cancer cells isolated from the tumors of patients with non-small cell lung cancer using competitive displacement of aptamers from EpCAM by a corresponding antibody. The resulting aptamers clones showed good nanomolar affinity to EpCAM-positive lung cancer cells. Confocal microscopy imaging and spectral profiling of lung cancer tissues confirmed the same protein target for the aptamers and anti-EpCAM antibodies. Furthermore, the resulted aptamers were successfully applied for isolation and detection of circulating tumor cells in clinical samples of peripheral blood of lung cancer patients.

We describe the preparation and characterization of an aptamer-based electrochemical sensor to lung cancer tumor markers in human blood. The highly reproducible aptamer sensing layer with a high density (up to 70% coverage) on the gold electrode was made. Electrochemical methods and confocal laser scanning microscopy were used to study the stability of the aptamer layer structure and binding ability. A new blocking agent, a thiolated oligonucleotide with an unrelated sequence, was applied to fill the aptamer layer’s defects. Electrochemical aptasensor signal processing was enhanced using deep learning and computer simulation of the experimental data array. It was found that the combinations (coupled and tripled) of cyclic voltammogram features allowed for distinguishing between the samples from lung cancer patients and healthy candidates with a mean accuracy of 0.73. The capacitive component from the non-Faradic electrochemical impedance spectroscopy data indicated the tumor marker’s presence in a sample. These findings allowed for the creation of highly informative aptasensors for early lung cancer diagnostics.

Temporal lobe epilepsy has various origins, involving or not involving structural changes in brain tissue. The mechanisms of epileptogenesis are associated with cell regulation and signaling disruptions expressed in varied levels of proteins. The blood plasma proteomic profiling of temporal lobe epilepsy patients (including magnetic resonance imaging (MRI)-positive and MRI-negative ones) and healthy volunteers using mass spectrometry and label-free quantification revealed a list of differently expressed proteins. Several apolipoproteins (APOA1, APOD, and APOA4), serpin protease inhibitors (SERPINA3, SERPINF1, etc.), complement components (C9, C8, and C1R), and a total of 42 proteins were found to be significantly upregulated in the temporal lobe epilepsy group. A classification analysis of these proteins according to their biological functions, as well as a review of the published sources, disclosed the predominant involvement of the processes mostly affected during epilepsy such as neuroinflammation, intracellular signaling, lipid metabolism, and oxidative stress. The presence of several proteins related to the corresponding compensatory mechanisms has been noted. After further validation, the newly identified temporal lobe epilepsy biomarker candidates may be used as epilepsy diagnostic tools, in addition to other less specific methods such as electroencephalography or MRI.

Background: Oncological diseases are a major focus in medicine, with millions diagnosed each year, leading researchers to seek new diagnostic and treatment methods. One promising avenue is the development of targeted therapies and rapid diagnostic tests using recognition molecules. The pharmaceutical industry is increasingly exploring nucleic acid-based therapeutics. However, producing long oligonucleotides, especially aptamers, poses significant production challenges. Objectives: This study aims to demonstrate the efficacy of using molecular modeling, supported by experimental procedures, for altering aptamer nucleotide sequences while maintaining their binding capabilities. The focus is on reducing production costs and enhancing binding dynamics by removing nonfunctional regions and minimizing nonspecific binding. Methods: A molecular modeling approach was employed to elucidate the structure of a DNA aptamer, Gli-55, facilitating the truncation of nonessential regions in the Gli-55 aptamer, which selectively binds to glioblastoma (GBM). This process aimed to produce a truncated aptamer, Gli-35, capable of forming similar structural elements to the original sequence with reduced nonspecific binding. The efficiency of the truncation was proved by flow cytometry, fluorescence polarization (FP), and confocal microscopy. Results: The molecular design indicated that the new truncated Gli-35 aptamer retained the structural integrity of Gli-55. In vitro studies showed that Gli-35 had a binding affinity comparable to the initial long aptamer while the selectivity increased. Gli-35 internalized inside the cell faster than Gli-55 and crossed the blood–brain barrier (BBB), as demonstrated in an in vitro model. Conclusions: The success of this truncation approach suggests its potential applicability in scenarios where molecular target information is limited. The study highlights a strategic and resource-efficient methodology for aptamer development. By employing molecular modeling and truncation, researchers can reduce production costs and avoid trial and error in sequence selection. This approach is promising for enhancing the efficiency of therapeutic agent development, particularly in cases lacking detailed molecular target insights.

Adipose tissue exhibits dynamic metabolic and structural changes in response to environmental stimuli, including temperature fluctuations. While continuous cold exposure has been extensively studied, the molecular effects of prolonged intermittent cold exposure (ICE) remain poorly characterized. Here, we present a proteomic analysis of inguinal white adipose tissue (IWAT) from mice subjected to a 16-week regimen of short-term daily ICE (6 °C for 6 h, 5 days per week) without compensatory caloric intake. Mass spectrometry identified 1108 proteins, with 140 differentially expressed between experimental and control groups. ICE significantly upregulated mitochondrial proteins associated with lipid and carbohydrate catabolism, the tricarboxylic acid (TCA) cycle, oxidative phosphorylation, and lipogenesis, including LETM1, AIFM1, PHB, PHB2, ACOT2, NDUA9, and ATP5J. These changes reflect enhanced metabolic activity and mitochondrial remodeling. In contrast, proteins linked to oxidative stress, insulin resistance, inflammation, and extracellular matrix remodeling were downregulated, such as HMGB1, FETUA, SERPH1, RPN1, and AOC3. Notably, gamma-synuclein (SYUG), which inhibits lipolysis, was undetectable in ICE-treated samples. Our findings support the hypothesis that ICE promotes thermogenic reprogramming and metabolic rejuvenation in subcutaneous fat through activation of futile cycles and mitochondrial restructuring. This study offers molecular insights into adaptive thermogenesis and presents intermittent cold exposure as a potential strategy to mitigate adipose tissue aging.