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Proteomic analysis of extracellular vesicles presents several challenges due to the unique nature of these small membrane-bound structures. Alternative analyses could reveal outcomes hidden from standard statistics to explore and develop potential new biological hypotheses that may have been overlooked during the initial evaluation of the data. An analysis sequence focusing on deviating protein expressions from donors' primary cells was performed, leveraging machine-learning techniques to analyze small datasets, and it has been applied to evaluate extracellular vesicles' protein content gathered from mesenchymal stem cells cultured on bioactive glass discs doped or not with metal ions. The goal was to provide additional opportunities for detecting details between experimental conditions that are not entirely revealed with classic statistical inference, offering further insights regarding the experimental design and assisting the researchers in interpreting the outcomes. The methodology extracted a set of EV-related proteins whose differences between conditions could be partially explainable with statistics, suggesting the presence of other factors involved in the bioactive glasses' interactions with tissues. Outlier identification of extracellular vesicles' protein expression levels related to biomaterial preparation was instrumental in improving the interpretation of the experimental outcomes.

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The present manuscript tested an automated analysis sequence to provide a decision support system to track the OCP synthesis from [Formula: see text]-TCP over time. Initially, the XRD and FTIR signals from a hundredfold scaled-up hydrolysis of OCP from [Formula: see text]-TCP were fused and modeled by the curve fitting based on the significantly established maxima from the literature and nine features extracted from the fitted shapes. Afterward, the analysis sequence enclosed the machine learning techniques for feature ranking, spatial filtering, and dimensionality reduction to support the automatic recognition of the synthesis stages. The proposed analysis pipeline for OCP identification might be the foundation for a decision support system explicitly targeting OCP synthesis. Future projects will exploit the suggested methodology for pinpointing the OCP production over time (including the intermediary phases present in the OCP formation) and for evaluating whether biological variables might be merged with biomaterial properties to build a unified model of tissue response to the implant.

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BACKGROUND: Establishing baseline measurements on normative data is essential to evaluate standards of care and the impact of clinical or surgical treatments. Hand volume determination is relevant in pathological conditions where the anatomical structures might undergo modifications like post-treatment chronic edema. For example, one of the consequences of breast cancer treatment is the possibility of developing uni-lateral lymphedema on the upper limbs. OBJECTIVE: Arm and forearm volumetrics are well-studied techniques, whereas hand volumetry computation poses several challenges both from the clinical and digital perspectives. The current work has explored routine clinical and customized digital methodologies for hand volume appraisal on healthy subjects. METHODS: Clinical hand volumes computed by water displacement or circumferential measurements were compared to digital volumetry calculated from 3D laser scans. Digital volume quantification algorithms exploited the gift wrapping concept or cubic tessellation of acquired 3D shapes. This latter digital technique is parametric, and a calibration methodology to define the resolution of the tessellation has been validated. RESULTS: Results on a group of normal subjects demonstrated that the volumes computed from digital hand representations extracted by tessellation return values similar to the clinical water displacement volume assessment at low tolerances. CONCLUSIONS: The current investigation suggested that the tessellation algorithm could be considered a digital equivalent of water displacement for hand volumetrics. Future studies are needed to confirm these results in people with lymphedema.

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Spiking neural networks are biologically inspired machine learning algorithms attracting researchers' attention for their applicability to alternative energy-efficient hardware other than traditional computers. In the current work, spiking neural networks have been tested in a quantitative structure-activity analysis targeting the toxicity of molecules. Multiple public-domain databases of compounds have been evaluated with spiking neural networks, achieving accuracies compatible with high-quality frameworks presented in the previous literature. The numerical experiments also included an analysis of hyperparameters and tested the spiking neural networks on molecular fingerprints of different lengths. Proposing alternatives to traditional software and hardware for time- and resource-consuming tasks, such as those found in chemoinformatics, may open the door to new research and improvements in the field.

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Background: Breast cancer-related lymphedema (BCRL) could be one consequence of breast cancer (BC). Although several risk factors have been identified, a predictive algorithm still needs to be made available to determine the patient's risk from an ensemble of clinical variables. Therefore, this study aimed to characterize the risk of BCRL by investigating the characteristics of autogenerated clusters of patients. Methods: The dataset under analysis was a multi-centric data collection of twenty-three clinical features from patients undergoing axillary dissection for BC and presenting BCRL or not. The patients' variables were initially analyzed separately in two low-dimensional embeddings. Afterward, the two models were merged in a bi-dimensional prognostic map, with patients categorized into three clusters using a Gaussian mixture model. Results: The prognostic map represented the medical records of 294 women (mean age: 59.823±12.879 years) grouped into three clusters with a different proportion of subjects affected by BCRL (probability that a patient with BCRL belonged to Cluster A: 5.71%; Cluster B: 71.42%; Cluster C: 22.86%). The investigation evaluated intra- and inter-cluster factors and identified a subset of clinical variables meaningful in determining cluster membership and significantly associated with BCRL biological hazard. Conclusions: The results of this study provide potential insight for precise risk assessment of patients affected by BCRL, with implications in prevention strategies, for instance, focusing the resources on identifying patients at higher risk.

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INTRODUCTION: Bladder cancer assessment with non-invasive gene expression signatures facilitates the detection of patients at risk and surveillance of their status, bypassing the discomforts given by cystoscopy. To achieve accurate cancer estimation, analysis pipelines for gene expression data (GED) may integrate a sequence of several machine learning and bio-statistical techniques to model complex characteristics of pathological patterns. METHODS: Numerical experiments tested the combination of GED preprocessing by discretization with tree ensemble embeddings and nonlinear dimensionality reductions to categorize oncological patients comprehensively. Modeling aimed to identify tumor stage and distinguish survival outcomes in two situations: complete and partial data embedding. This latter experimental condition simulates the addition of new patients to an existing model for rapid monitoring of disease progression. Machine learning procedures were employed to identify the most relevant genes involved in patient prognosis and test the performance of preprocessed GED compared to untransformed data in predicting patient conditions. RESULTS: Data embedding paired with dimensionality reduction produced prognostic maps with well-defined clusters of patients, suitable for medical decision support. A second experiment simulated the addition of new patients to an existing model (partial data embedding): Uniform Manifold Approximation and Projection (UMAP) methodology with uniform data discretization led to better outcomes than other analyzed pipelines. Further exploration of parameter space for UMAP and t-distributed stochastic neighbor embedding (t-SNE) underlined the importance of tuning a higher number of parameters for UMAP rather than t-SNE. Moreover, two different machine learning experiments identified a group of genes valuable for partitioning patients (gene relevance analysis) and showed the higher precision obtained by preprocessed data in predicting tumor outcomes for cancer stage and survival rate (six classes prediction). CONCLUSIONS: The present investigation proposed new analysis pipelines for disease outcome modeling from bladder cancer-related biomarkers. Complete and partial data embedding experiments suggested that pipelines employing UMAP had a more accurate predictive ability, supporting the recent literature trends on this methodology. However, it was also found that several UMAP parameters influence experimental results, therefore deriving a recommendation for researchers to pay attention to this aspect of the UMAP technique. Machine learning procedures further demonstrated the effectiveness of the proposed preprocessing in predicting patients' conditions and determined a sub-group of biomarkers significant for forecasting bladder cancer prognosis.

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In medicine, tridimensional scanning devices produce digital surfaces that replicate the bodies of patients, facilitating anthropometric measurement and limb volume quantification in pathological conditions. Free programs that address this task are not commonly found, with doctors mainly relying on proprietary software. This aspect brings reduced reproducibility of studies and evaluation of alternative measures. A software package made up of three programs has been developed and released together with supporting materials to enhance reproducibility and comparisons between medical centers. In this article, the functions of the programs and steps for volume assessment were introduced together with a pilot study for upper limb volume quantification. This initial experiment aimed to also verify the performance of digital volumes derived from the convex-hull gift-wrapping algorithm and the alternative analysis methods enclosed in the software. Few of these digital volumes are parameter-dependent, requiring a value selection. The experiment was conducted on a small mixed-gender group of young adults without correction for factors like arm dominance or specific physical training. In the sample under investigation, the analysis confirmed the substantial agreement between the clinical and current configurations of digital volumes produced by the package (R 2 interval from 0.93 to 0.97, r ranged from 0.965 to 0.984); in addition, as a general consideration, gender appears as a variable that could influence upper limb volume quantification if a single model is built.

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When comparing the digits of different physical sizes, the processing of numerical value interacts with the processing of physical size. Given the universal use of Arabic numbers in mathematics and daily life, this study aims to elucidate the cognitive processes involved in the interactions of task-relevant and task-irrelevant features during information processing. We investigated this question by examining event-related potential (ERP) using a modified version of the size congruity comparison, which is a Stroop-like task. Numerical value and physical size were varied independently under task-relevant and task-irrelevant conditions. To better examine how the task-irrelevant features modulated the processing of the task-relevant attributes, a neutral condition was included in both tasks. For the physical task, congruent trials showed a less negative N200 response than neutral trials (indicating a facilitation effect), and incongruent trials elicited a larger N450 and smaller late positive complex (LPC) response than neutral trials (indicating an interference effect). For the numerical task, congruent trials showed a larger LPC response than neutral trials (indicating a facilitation effect). These ERP findings indicate that the sources of the facilitation and interference effects appear in different cognitive processes for each task. We further suggest that language characteristics may be a factor in the superior numerical processing exhibited in this study.

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Tracking emotional responses as they unfold has been one of the hallmarks of applied neuroscience and related disciplines, but recent studies suggest that automatic tracking of facial expressions have low validation. In this study, we focused on the direct measurement of facial muscles involved in expressions such as smiling. We used single-channel surface electromyography (sEMG) to evaluate the muscular activity from the Zygomaticus Major face muscle while participants watched music videos. Participants were then tasked with rating each video with regard to their thoughts and responses to each of them, including their judgment of emotional tone ("Valence"), personal preference ("Liking") and rating of whether the video displayed strength and impression ("Dominance"). Using a minimal recording setup, we employed three ways to characterize muscular activity associated with spontaneous smiles. The total time spent smiling (ZygoNum), the average duration of smiles (ZygoLen), and instances of high valence (ZygoTrace). Our results demonstrate that Valence was the emotional dimension that was most related to the Zygomaticus activity. Here, the ZygoNum had higher discriminatory power than ZygoLen for Valence quantification. An additional investigation using fractal properties of sEMG time series confirmed previous studies of the Facial Action Coding System (FACS) documenting a smoother contraction of facial muscles for enjoyment smiles. Further analysis using ZygoTrace responses over time to the video events discerned "high valence" stimuli with a 76% accuracy. Additional validation of this approach came against previous findings on valence detection using features derived from a single channel EEG setup. We discuss these results in light of both the recent replication problems of facial expression measures, and in relation to the need for methods to reliably assess emotional responses in more challenging conditions, such as Virtual Reality, in which facial expressions are often covered by the equipment used.

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The ability to detect an incoming visual stimulus is enhanced by knowledge of stimulus location (orienting of visuospatial attention). Although the brain mechanisms at the basis of this enhancement are not yet fully clarified, there is evidence that orienting of attention is accompanied by the activation of oculomotor circuits. It remains unclear, however, whether this oculomotor activity is an epiphenomenon or is functionally related to the attentional process. Attentional benefits are usually measured by the classical Posner paradigm. When subjects fixate centrally and are requested to detect a visual stimulus that could appear in an attended or unattended location, they react faster to stimuli appearing in the attended one. Here, we demonstrate that in monocular vision visuospatial attention was significantly modulated by the position of the eye in the orbit. When the screen was placed 40 degrees to the right or to the left of subjects' sagittal plane, attentional benefits for stimuli appearing in subjects' temporal spatial hemifield dramatically decayed, even if the retinal stimulation was exactly the same as in the classical paradigm. The finding that eyes and attention show a common limit stop point supports their close functional coupling.

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