Abstract Introduction Electroencephalography (EEG) is widely employed for the automatic detection of Major Depressive Disorder (MDD), with most approaches focusing primarily on neural activity while discarding non-brain-derived components as artefacts. However, these non-neural sources may contain clinically relevant information, as they reflect the output of underlying neurophysiological processes. Material and methods In this study, we propose an EEG analysis framework that leverages components traditionally labeled as artefacts. Independent Component Analysis (ICA) was applied to a publicly available dataset comprising 34 subjects with depressive symptoms and 30 healthy controls to extract three categories of independent components (ICs): posterior brain ICs (Pb ICs), ocular ICs (Eye ICs), and electromyographic ICs (EMG ICs). Features were extracted using the Wavelet Scattering Transform (WST), and classification was performed using a Long Short-Term Memory (LSTM) network. Model performance was evaluated across multiple values of the invariance scale (IS) parameter using a subject-level holdout split. Results The results indicate that classification performance was strongly dependent on the invariance scale (IS) parameter. Ocular ICs consistently provided higher discriminative performance across several configurations, whereas EMG ICs showed limited sensitivity. In contrast, posterior brain ICs did not exhibit meaningful discriminative performance, suggesting that reliable class-separating patterns could not be learned under the current experimental setup. Conclusions These findings suggest that non-neural EEG components, particularly ocular activity extracted via ICA, may contain useful information for MDD classification. However, given the use of a single subject-level split and the relatively small dataset size, the results should be considered preliminary. Further studies using more robust validation strategies and larger datasets are required to confirm the generalizability of this approach.
Abstract Introduction The aim of this study was to investigate the acute effects of a single leg press resistance training session, performed at different intensities and under different conditions (normoxia and normobaric hypoxia), on blood serum proteins assessed by differential scanning calorimetry (DSC). Material and methods Fourteen resistance-trained men participated in the trial. Each subject completed three sessions: one familiarization session and two experimental sessions separated by one week. All sessions were conducted at the same time of day (9:00–11:00) to control for circadian influences. Blood samples were collected at rest (baseline, BA) and immediately post-exercise (POST). Serum samples were analyzed using DSC. Results DSC profiles revealed post-exercise changes in serum protein denaturation. Statistically significant differences were observed between pre- and post-exercise states for parameters of the endothermic transition associated with protein denaturation. Conclusions The findings demonstrate the feasibility of using DSC to monitor the effects of exercise performed at different intensities and under varying oxygen conditions (normoxia and normobaric hypoxia) on serum protein changes.
Abstract Purpose To compare target coverage, selected plan-quality indices, organ-at-risk (OAR) dose parameters, and planned beam-on delivery time among coplanar VMAT (C-VMAT), non-coplanar VMAT (NC-VMAT), unblocked helical tomo-therapy (UB-HT), and directionally blocked helical tomotherapy (DB-HT) in lung stereotactic body radiotherapy (SBRT). Methods Ten patients with early-stage non-small cell lung cancer or oligometastatic lung lesions were retrospectively replanned. For each patient, seven plans were generated using the same planning CT dataset and structure set: C-VMAT, four NC-VMAT configurations with couch rotations of 45°, 60°, 75°, and 90°, UB-HT, and DB-HT. Plans were evaluated using target coverage, gradient index (GI), conformity index (CI), homogeneity index (HI), selected OAR dose metrics, and planned beam-on time. Comparisons were performed using the Friedman test. Results All techniques achieved clinically acceptable target coverage, with mean PTV V50 coverage close to the planning objective. Although PTV V50 showed a statistically significant overall difference, the absolute differences were small. Significant overall differences were observed for GI and HI, whereas CI did not differ significantly. Planned beam-on time showed the clearest technique-related difference, with VMAT-based plans requiring substantially shorter times than HT-based plans. HT-based plans, particularly DB-HT, showed lower mean lung V20 and mean lung dose values. DB-HT also showed the lowest mean heart and aorta D0.1cc values; however, these findings were interpreted as descriptive overall differences rather than pairwise evidence of superiority. Conclusion Under standardized planning conditions, all evaluated techniques produced acceptable lung SBRT plans. VMAT-based techniques provided shorter planned beam-on times, whereas DB-HT showed lower mean values for selected lung and mediastinal OAR parameters. Larger studies with anatomical stratification and spatial gradient metrics are needed to clarify the clinical relevance of these dosimetric differences.
Abstract Introduction : Remote ischemic conditioning (RIPC) is a simple, non-invasive procedure in which brief episodes of ischemia and reperfusion, applied to a limb, induce systemic protective mechanisms against ischemic injury to distant organs. Growing experimental evidence suggests that these protective effects may involve complex interactions between neural, hormonal, and endothelial pathways, with modulation of autonomic nervous system activity and vascular function being key contributing factors. Despite increasing interest in the physiological mechanisms underlying RIPC, a comprehensive characterization of its acute effects on autonomic homeostasis and vascular characteristics in humans remains limited. Therefore, this study aimed to investigate the immediate effects of RIPC on autonomic regulation and peripheral vascular stiffness in healthy adults using simultaneous electrocardiography (ECG) and digital pulse wave (DPW) analysis. Methods : Fifteen healthy male participants with normal blood pressure underwent a standardized RIPC protocol consisting of four cycles of upper arm ischemia and reperfusion. A sham remote non-ischemic preconditioning protocol (RNIPC) was used as a control. Five-minute ECG and fingertip DPW recordings were obtained before and immediately after each intervention. Heart rate variability (HRV), large-artery stiffness index (SI), and second-derivative b/a ratio were quantified. Results : RIPC was associated with HRV changes compatible with a shift toward enhanced vagal modulation and/or reduced sympathetic influence, reflected by increases in time-domain, frequency-domain, and non-linear HRV indices, alongside reductions in heart rate and stress index. Peripheral vascular stiffness decreased, and the b/a ratio became more negative. Conclusions : In this exploratory study, acute RIPC was associated with HRV changes compatible with enhanced vagal modulation and/or reduced sympathetic influence, together with reductions in peripheral vascular stiffness indices. ECG-gated DPW analysis provides a sensitive, non-invasive approach for detecting these physiological adaptations in humans.
Abstract The diagnosis of breast neoplasms by ultrasound examination depends on the subjective interpretation of the radiologist, which determines the need to develop objective methods of computer analysis to standardise the diagnostic process. The aim of this study was to determine the diagnostic performance of machine learning algorithms for classifying benign and malignant lesions in breast ultrasound through a systematic literature review with quantitative meta-analysis. A systematic search was conducted in PubMed, Scopus and Embase in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, identifying 57 studies published between 2019 and 2025, of which 49 were included in the meta-analysis using a bivariate model and quality assessment with the Quality Assessment of Diagnostic Accuracy Studies tool. The pooled sensitivity was 91.1% (95% confidence interval 88.9-93.0%), specificity 85.3% (95% confidence interval 82.9-87.5%), area under the receiver operating characteristic curve 0.957 (95% confidence interval 0.941-0.973), and diagnostic odds ratio 64.8 (95% confidence interval 46.1-91.2). Subgroup analysis showed that deep learning demonstrated higher specificity compared with commercial systems (87.2% versus 81.5%, p=0.041), a multimodal approach provided a 3.9% gain (p<0.001), artificial intelligence assistance was most effective for junior radiologists with an improvement of 0.164 (p<0.001), and use for reclassification of BI-RADS 4A lesions allowed biopsy reduction of 67.8-79.1% with a negative predictive value of 96.7%. The findings support clinical implementation of artificial intelligence systems for standardising diagnosis and optimising biopsy indications. The practical significance lies in reducing unnecessary invasive procedures and improving the diagnostic performance of less experienced radiologists.
