Deep learning (DL) is emerging as a successful technique for automatic detection and differentiation of spontaneous seizures that may otherwise be missed or misclassified. Herein, we propose a system architecture based on top-performing DL models for binary and multigroup classifications with the non-overlapping window technique, which we tested on the TUSZ dataset. The system accurately detects seizure episodes (87.7% Sn, 91.16% Sp) and carefully distinguishes eight seizure types (95–100% Acc). An increase in EEG sampling rate from 50 to 250 Hz boosted model performance: the precision of seizure detection rose by 5%, and seizure differentiation by 7%. A low sampling rate is a reasonable solution for training reliable models with EEG data. Decreasing the number of EEG electrodes from 21 to 8 did not affect seizure detection but worsened seizure differentiation significantly: 98.24 ± 0.17 vs. 85.14 ± 3.14% recall. In detecting epileptic episodes, all electrodes provided equally informative input, but in seizure differentiation, their informative value varied. We improved model explainability with interpretable ML. Activation maximization highlighted the presence of EEG patterns specific to eight seizure types. Cortical projection of epileptic sources depicted differences between generalized and focal seizures. Interpretable ML techniques confirmed that our system recognizes biologically meaningful features as indicators of epileptic activity in EEG.

A high incidence and prevalence of neurodegenerative diseases and neurodevelopmental disorders justify the necessity of well-defined criteria for diagnosing these pathologies from brain imaging findings. No easy-to-apply quantitative markers of abnormal brain development and ageing are available. We aim to find the characteristic features of non-pathological development and degeneration in distinct brain structures and to work out a precise descriptive model of brain morphometry in age groups. We will use four biomedical databases to acquire original peer-reviewed publications on brain structural changes occurring throughout the human life-span. Selected publications will be uploaded to Covidence systematic review software for automatic deduplication and blinded screening. Afterwards, we will manually review the titles, abstracts, and full texts to identify the papers matching eligibility criteria. The relevant data will be extracted to a ‘Summary of findings’ table. This will allow us to calculate the annual rate of change in the volume or thickness of brain structures and to model the lifelong dynamics in the morphometry data. Finally, we will adjust the loss of weight/thickness in specific brain areas to the total intracranial volume. The systematic review will synthesise knowledge on structural brain change across the life-span.

Background and Objectives: Surgical treatment is central to breast cancer management; however, its long-term impact on QoL varies substantially among patients. This study sought to model the dynamic trajectories of postoperative QoL following breast-conserving surgery (BCS), mastectomy with immediate reconstruction (Mx+IR), and mastectomy alone (MA). It also examined how these trajectories varied across different age groups and over time. Materials and Methods: The review and meta-analysis identified 150 peer-reviewed studies reporting QoL outcomes using validated instruments (EORTC QLQ-C30 or BREAST-Q). A total of 123 observations from 45 studies were included for analysis of global QoL. We standardized QoL scores to a 0–100 scale and harmonized postoperative assessments across six time intervals, extending to more than 73 months. Multilevel random-effects models evaluated linear, quadratic, and logarithmic functions. Subgroup analyses and meta-regressions assessed the moderating effects of surgical type and age. Results: BCS showed the steepest QoL gains, followed by Mx+IR, while MA had the lowest scores and slowest recovery. Compared to BCS, MA showed significantly poorer and delayed recovery, and Mx+IR showed a smaller, borderline decrease. All groups displayed modest long-term QoL plateauing. Conclusions: Global QoL after breast cancer surgery follows distinct, time-dependent patterns shaped by surgical approach and age. These findings emphasize the importance of discussing patients’ quality-of-life expectations with them so that survivorship care can be personalized to their needs.

Background: Genetic and epigenetic changes, oxidative stress and inflammation influence the rate of aging, which diseases, lifestyle and environmental factors can further accelerate. In accelerated aging (AA), the biological age exceeds the chronological age. Objective: The objective of this study is to reappraise the AA concept critically, considering its weaknesses and limitations. Methods: We reviewed more than 300 recent articles dealing with the physiology of brain aging and neurodegeneration pathophysiology. Results: (1) Application of the AA concept to individual organs outside the brain is challenging as organs of different systems age at different rates. (2) There is a need to consider the deceleration of aging due to the potential use of the individual structure–functional reserves. The latter can be restored by pharmacological and/or cognitive therapy, environment, etc. (3) The AA concept lacks both standardised terminology and methodology. (4) Changes in specific molecular biomarkers (MBM) reflect aging-related processes; however, numerous MBM candidates should be validated to consolidate the AA theory. (5) The exact nature of many potential causal factors, biological outcomes and interactions between the former and the latter remain largely unclear. Conclusions: Although AA is commonly recognised as a perspective theory, it still suffers from a number of gaps and limitations that assume the necessity for an updated AA concept.

Background: The prevalence of sexual dysfunctions varies from 35.5% to over 80% among postpartum women. Controversy exists regarding the risk factors for female sexual dysfunction (FSD) in the postpartum period. It remains unclear whether breastfeeding types contribute to the development of FSDs differently. Aims: The primary goal of this meta-analysis was to explore the role of baby feeding practices in developing sexual dysfunctions in women. Methods: We conducted a systematic literature search using the biomedical databases Scopus, CINAHL, Embase, the Web of Science, and PubMed/Medline. We looked for peer-reviewed, original studies written in English, Polish, and Arabic and published between January 2000 and June 2023. We included publications that reported scores in sexuality domains assessed with the Female Sexual Dysfunction Index (FSFI) and any sexuality-related issues during postpartum. The FSFI scores were combined in a meta-analysis using the random-effects inverse-variance model. Other findings were synthesized with a narrative review. Results: Eighteen articles met the eligibility criteria for the systematic review and meta-analysis. Sexual dysfunctions were detected in all the women, irrespective of the feeding type. Better overall sexual functioning was reported among women using complementary feeding than among those who breastfed exclusively or used baby bottles: 22.16, 95% CI [21.68; 22.65]; 21.61, 95% CI [20.27; 22.95]; and 20.18, 95% CI: [20.93; 61.30], respectively. Slightly lower scores were reported in all the FSFI subscales in exclusively breastfeeding women compared to those using the complementary method. Conclusions: Breastfeeding females experience difficulties in sexual life during the postpartum period, irrespective of the feeding type. These findings can help in designing preventive measures for tackling postpartum sexual dysfunctions in women.

Background The number of patients diagnosed with multiple sclerosis (MS) has increased significantly over the last decade. The challenge is to identify the transition from relapsing-remitting to secondary progressive MS. Since available methods to examine patients with MS are limited, both the diagnostics and prognostication of disease progression would benefit from the multimodal approach. The latter combines the evidence obtained from disparate radiologic modalities, neurophysiological evaluation, cognitive assessment and molecular diagnostics. In this systematic review we will analyse the advantages of multimodal studies in predicting the risk of conversion to secondary progressive MS. Methods and analysis We will use peer-reviewed publications available in Web of Science, Medline/PubMed, Scopus, Embase and CINAHL databases. In vivo studies reporting the predictive value of diagnostic methods will be considered. Selected publications will be processed through Covidence software for automatic deduplication and blind screening. Two reviewers will use a predefined template to extract the data from eligible studies. We will analyse the performance metrics (1) for the classification models reflecting the risk of secondary progression: sensitivity, specificity, accuracy, area under the receiver operating characteristic curve, positive and negative predictive values; (2) for the regression models forecasting disability scores: the ratio of mean absolute error to the range of values. Then, we will create ranking charts representing performance of the algorithms for calculating disability level and MS progression. Finally, we will compare the predictive power of radiological and radiomical correlates of clinical disability and cognitive impairment in patients with MS. Ethics and dissemination The study does not require ethical approval because we will analyse publicly available literature. The project results will be published in a peer-review journal and presented at scientific conferences. PROSPERO registration number CRD42022354179.