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Hydrogels are polymeric materials that possess a set of characteristics meeting various requirements of an ideal wound dressing, making them promising for wound care. These features include, among others, the ability to absorb and retain large amounts of water and the capacity to closely mimic native structures, such as the extracellular matrix, facilitating various cellular processes like proliferation and differentiation. The polymers used in hydrogel formulations exhibit a broad spectrum of properties, allowing them to be classified into two main categories: natural polymers like collagen and chitosan, and synthetic polymers such as polyurethane and polyethylene glycol. This review offers a comprehensive overview and critical analysis of the key polymers that can constitute hydrogels, beginning with a brief contextualization of the polymers. It delves into their function, origin, and chemical structure, highlighting key sources of extraction and obtaining. Additionally, this review encompasses the main intrinsic properties of these polymers and their roles in the wound healing process, accompanied, whenever available, by explanations of the underlying mechanisms of action. It also addresses limitations and describes some studies on the effectiveness of isolated polymers in promoting skin regeneration and wound healing. Subsequently, we briefly discuss some application strategies of hydrogels derived from their intrinsic potential to promote the wound healing process. This can be achieved due to their role in the stimulation of angiogenesis, for example, or through the incorporation of substances like growth factors or drugs, such as antimicrobials, imparting new properties to the hydrogels. In addition to substance incorporation, the potential of hydrogels is also related to their ability to serve as a three-dimensional matrix for cell culture, whether it involves loading cells into the hydrogel or recruiting cells to the wound site, where they proliferate on the scaffold to form new tissue. The latter strategy presupposes the incorporation of biosensors into the hydrogel for real-time monitoring of wound conditions, such as temperature and pH. Future prospects are then ultimately addressed. As far as we are aware, this manuscript represents the first comprehensive approach that brings together and critically analyzes fundamental aspects of both natural and synthetic polymers constituting hydrogels in the context of cutaneous wound healing. It will serve as a foundational point for future studies, aiming to contribute to the development of an effective and environmentally friendly dressing for wounds.

Functional magnetic resonance imaging (fMRI) is the technique of choice for detecting large-scale functional brain networks and to investigate their dynamics. Because fMRI measures brain activity indirectly, electroencephalography (EEG) has been recently considered a feasible tool for detecting such networks, particularly the resting-state networks (RSNs). However, a truly unbiased validation of such claims is still missing, which can only be accomplished by using simultaneously acquired EEG and fMRI data, due to the spontaneous nature of the activity underlying the RSNs. Additionally, EEG is still poorly explored for the purpose of mapping task-specific networks, and no studies so far have been focused on investigating networks' dynamic functional connectivity (dFC) with EEG. Here, we started by validating RSNs derived from the continuous reconstruction of EEG sources by directly comparing them with those derived from simultaneous fMRI data of 10 healthy participants, and obtaining an average overlap (quantified by the Dice coefficient) of 0.4. We also showed the ability of EEG to map the facial expressions processing network (FEPN), highlighting regions near the posterior superior temporal sulcus, where the FEPN is anchored. Then, we measured the dFC using EEG for the first time in this context, estimated dFC brain states using dictionary learning, and compared such states with those obtained from the fMRI. We found a statistically significant match between fMRI and EEG dFC states, and determined the existence of two matched dFC states which contribution over time was associated with the brain activity at the FEPN, showing that the dynamics of FEPN can be captured by both fMRI and EEG. Our results push the limits of EEG toward being used as a brain imaging tool, while supporting the growing literature on EEG correlates of (dynamic) functional connectivity measured with fMRI, and providing novel insights into the coupling mechanisms underlying the two imaging techniques.

Parkinsons Disease (PD) is the second most common neurodegenerative disease worldwide, and is characterized by a progressive degeneration of dopaminergic neurons. Without an effective treatment, it is crucial to find new therapeutic options to fight the neurodegenerative process, which may arise from marine resources. Accordingly, the goal of the present work was to evaluate the ability of the monoterpenoid lactone Loliolide, isolated from the green seaweed Codium tomentosum, to prevent neurological cell death mediated by the neurotoxin 6-hydroxydopamine (6-OHDA) on SH-SY5Y cells and their anti-inflammatory effects in RAW 264.7 macrophages. Loliolide was obtained from the diethyl ether extract, purified through column chromatography and identified by NMR spectroscopy. The neuroprotective effects were evaluated by the MTT method. Cells’ exposure to 6-OHDA in the presence of Loliolide led to an increase of cells’ viability in 40%, and this effect was mediated by mitochondrial protection, reduction of oxidative stress condition and apoptosis, and inhibition of the NF-kB pathway. Additionally, Loliolide also suppressed nitric oxide production and inhibited the production of TNF-α and IL-6 pro-inflammatory cytokines. The results suggest that Loliolide can inspire the development of new neuroprotective therapeutic agents and thus, more detailed studies should be considered to validate its pharmacological potential.

Seaweeds are an important source of nutrients and bioactive compounds and have a high potential as health boosters in aquaculture. This study evaluated the effect of dietary inclusion of Gracilaria gracilis biomass or its extract on the European seabass ( Dicentrarchus labrax ) gut microbial community. Juvenile fish were fed a commercial-like diet with 2.5% or 5% seaweed biomass or 0.35% seaweed extract for 47 days. The gut microbiome was assessed by 16S rRNA amplicon sequencing, and its diversity was not altered by the seaweed supplementation. However, a reduction in Proteobacteria abundance was observed. Random forest analysis highlighted the genera Photobacterium , Staphylococcus , Acinetobacter , Micrococcus and Sphingomonas, and their abundances were reduced when fish were fed diets with algae. SparCC correlation network analysis suggested several mutualistic and other antagonistic relationships that could be related to the predicted altered functions. These pathways were mainly related to the metabolism and biosynthesis of protective compounds such as ectoine and were upregulated in fish fed diets supplemented with algae. This study shows the beneficial potential of Gracilaria as a functional ingredient through the modulation of the complex microbial network towards fish health improvement.

Data scarcity in medical images makes transfer learning a common approach in computer-aided diagnosis. Some disease classification tasks can rely on large homogeneous public datasets to train the transferred model, while others cannot, i.e., endoscopic laryngeal cancer image identification. Distinguished from most current works, this work pioneers exploring a two-step heterogeneous transfer learning (THTL) framework for laryngeal cancer identification and summarizing the fundamental principles for the intermediate domain selection. For heterogeneity and clear vascular representation, diabetic retinopathy images were chosen as THTL’s intermediate domain. The experiment results reveal two vital principles in intermediate domain selection for future studies: 1) the size of the intermediate domain is not a sufficient condition to improve the transfer learning performance; 2) even distinct vascular features in the intermediate domain do not guarantee improved performance in the target domain. We observe that radial vascular patterns benefit benign classification, whereas twisted and tangled patterns align more with malignant classification. Additionally, to compensate for the absence of twisted patterns in the intermediate domains, we propose the Step-Wise Fine-Tuning (SWFT) technique, guided by the Layer Class Activate Map (LayerCAM) visualization result, getting 20.4% accuracy increases compared to accuracy from THTL’s, even higher than fine-tune all layers.

Cognitive human error and recent cognitive taxonomy on human error causes of software defects support the intuitive idea that, for instance, mental overload, attention slips, and working memory overload are important human causes for software bugs. In this paper, we approach the EEG as a reliable surrogate to MRI-based reference of the programmer’s cognitive state to be used in situations where heavy imaging techniques are infeasible. The idea is to use EEG biomarkers to validate other less intrusive physiological measures, that can be easily recorded by wearable devices and useful in the assessment of the developer’s cognitive state during software development tasks. Herein, our EEG study, with the support of fMRI, presents an extensive and systematic analysis by inspecting metrics and extracting relevant information about the most robust features, best EEG channels and the best hemodynamic time delay in the context of software development tasks. From the EEG-fMRI similarity analysis performed, we found significant correlations between a subset of EEG features and the Insula region of the brain, which has been reported as a region highly related to high cognitive tasks, such as software development tasks. We concluded that despite a clear inter-subject variability of the best EEG features and hemodynamic time delay used, the most robust and predominant EEG features, across all the subjects, are related to the Hjorth parameter Activity and Total Power features, from the EEG channels F4, FC4 and C4, and considering in most of the cases a hemodynamic time delay of 4 seconds used on the hemodynamic response function. These findings should be taken into account in future EEG-fMRI studies in the context of software debugging.

Classification of neural signals at the single-trial level and the study of their relevance in affective and cognitive neuroscience are still in their infancy. Here we investigated the neurophysiological correlates of conditions of increasing social scene complexity using 3D human models as targets of attention, which may also be important in autism research. Challenging single-trial statistical classification of EEG neural signals was attempted for detection of oddball stimuli with increasing social scene complexity. Stimuli had an oddball structure and were as follows: 1) flashed schematic eyes, 2) simple 3D faces flashed between averted and non-averted gaze (only eye position changing), 3) simple 3D faces flashed between averted and non-averted gaze (head and eye position changing), 4) animated avatar alternated its gaze direction to the left and to the right (head and eye position), 5) environment with 4 animated avatars all of which change gaze and one of which is the target of attention. We found a late (> 300 ms) neurophysiological oddball correlate for all conditions irrespective of their complexity as assessed by repeated measures ANOVA. We attempted single-trial detection of this signal with automatic classifiers and obtained a significant balanced accuracy classification of around 79%, which is noteworthy given the amount of scene complexity. Lateralization analysis showed a specific right lateralization only for more complex realistic social scenes. In sum, complex ecological animations with social content elicit neurophysiological events which can be characterized even at the single-trial level. These signals are right lateralized. These finding paves the way for neuroscientific studies in affective neuroscience based on complex social scenes, and given the detectability at the single trial level this suggests the feasibility of brain computer interfaces that can be applied to social cognition disorders such as autism.

Autism is characterised by core differences in social communication and interaction. The neurobiology underlying autism can be investigated using experimental designs that capture the dynamic nature of social perception, which activates the third visual pathway. Here, we investigated dynamic specific facial emotion processing using a naturalistic facial expression paradigm, leading to a specific dynamic N170 (dN170) evoked by emotion expression trajectories. Participants engaged in an active task of an avatar with two temporal trajectories: morphing from neutral to happy or sad expressions and unmorphing back to neutral. We recorded event-related potentials (ERPs) and magnetic resonance spectroscopy in autistic and non-autistic children and adolescents ( n  = 16 per group; ages between 8 and 17) matched for sex, handedness, and age. Results revealed that dN170 exhibited longer latencies during unmorphing for the autistic group. This specific timing effect, identified for the unmorphing versus morphing conditions in autism, suggests a stimulus trajectory-dependent effect (hysteresis). Dynamic P300 showed higher amplitudes in the autistic group during morphing, confirming the presence of an attentional compensatory mechanism. Correlations between ERP properties, GABA, and social communication abilities provided evidence of a dimensional continuum from non-autistic to autistic traits. These findings highlight the promising role of these ERPs as indicators of perceptual and attentional processing differences in autism.

Ultra-short-term HRV features assess minor autonomous nervous system variations such as variations resulting from cognitive stress peaks during demanding tasks. Several studies compare ultra-short-term and short-term HRV measurements to investigate their reliability. However, existing experiments are conducted in low cognitively demanding environments. In this paper, we propose to evaluate these measurements’ reliability under cognitively demanding tasks using a near real-life setting. For this purpose, we selected 31 HRV features, extracted from data collected from 21 programmers performing code comprehension, and compared them across 18 different time frames, ranging from 3 min to 10 s. Statistical significance and correlation tests were performed between the features extracted using the larger window (3 min) and the same features extracted with the other 17 time frames. We paired these analyses with Bland–Altman plots to inspect how the extraction window size affects the HRV features. The main results show 13 features that presented at least 50% correlation when using 60-second windows. The HF and mNN features achieved around 50% correlation using a 30-second window. The 30-second window was the smallest time frame considered to have reliable measurements. Furthermore, the mNN feature proved to be quite robust to the shortening of the time resolution.

Deficits in the interpretation of others' intentions from gaze-direction or other social attention cues are well-recognized in ASD. Here we investigated whether an EEG brain computer interface (BCI) can be used to train social cognition skills in ASD patients. We performed a single-arm feasibility clinical trial and enrolled 15 participants (mean age 22y 2m) with high-functioning ASD (mean full-scale IQ 103). Participants were submitted to a BCI training paradigm using a virtual reality interface over seven sessions spread over 4 months. The first four sessions occurred weekly, and the remainder monthly. In each session, the subject was asked to identify objects of interest based on the gaze direction of an avatar. Attentional responses were extracted from the EEG P300 component. A final follow-up assessment was performed 6-months after the last session. To analyze responses to joint attention cues participants were assessed pre and post intervention and in the follow-up, using an ecologic \"Joint-attention task.\" We used eye-tracking to identify the number of social attention items that a patient could accurately identify from an avatar's action cues (e.g., looking, pointing at). As secondary outcome measures we used the Autism Treatment Evaluation Checklist (ATEC) and the Vineland Adaptive Behavior Scale (VABS). Neuropsychological measures related to mood and depression were also assessed. In sum, we observed a decrease in total ATEC and rated autism symptoms (Sociability; Sensory/Cognitive Awareness; Health/Physical/Behavior); an evident improvement in Adapted Behavior Composite and in the DLS subarea from VABS; a decrease in Depression (from POMS) and in mood disturbance/depression (BDI). BCI online performance and tolerance were stable along the intervention. Average P300 amplitude and alpha power were also preserved across sessions. We have demonstrated the feasibility of BCI in this kind of intervention in ASD. Participants engage successfully and consistently in the task. Although the primary outcome (rate of automatic responses to joint attention cues) did not show changes, most secondary neuropsychological outcome measures showed improvement, yielding promise for a future efficacy trial. (clinical-trial ID: NCT02445625-clinicaltrials.gov).