Chronic wounds represent a major public health issue, with an extremely high cost worldwide. In healthy individuals, the wound healing process takes place in different stages: inflammation, cell proliferation (fibroblasts and keratinocytes of the dermis), and finally remodeling of the extracellular matrix (equilibrium between metalloproteinases and their inhibitors). In chronic wounds, the chronic inflammation favors exudate persistence and bacterial film has a special importance in the dynamics of chronic inflammation in wounds that do not heal. Recent advances in biopolymer-based materials for wound healing highlight the performance of specific alginate forms. An ideal wound dressing should be adherent to the wound surface and not to the wound bed, it should also be non-antigenic, biocompatible, semi-permeable, biodegradable, elastic but resistant, and cost-effective. It has to give protection against bacterial, infectious, mechanical, and thermal agents, to modulate the level of wound moisture, and to entrap and deliver drugs or other molecules This paper explores the roles of alginates in advanced wound-dressing forms with a particular emphasis on hydrogels, nanofibers networks, 3D-scaffolds or sponges entrapping fibroblasts, keratinocytes, or drugs to be released on the wound-bed. The latest research reports are presented and supported with in vitro and in vivo studies from the current literature.

Hemorrhage is a detrimental event present in traumatic injury, surgery, and disorders of bleeding that can become life-threatening if not properly managed. Moreover, uncontrolled bleeding can complicate surgical interventions, altering the outcome of surgical procedures. Therefore, to reduce the risk of complications and decrease the risk of morbidity and mortality associated with hemorrhage, it is necessary to use an effective hemostatic agent that ensures the immediate control of bleeding. In recent years, there have been increasingly rapid advances in developing a novel generation of biomaterials with hemostatic properties. Nowadays, a wide array of topical hemostatic agents is available, including chitosan-based biomaterials that have shown outstanding properties such as antibacterial, antifungal, hemostatic, and analgesic activity in addition to their biocompatibility, biodegradability, and wound-healing effects. This review provides an analysis of chitosan-based hemostatic biomaterials and discusses the progress made in their performance, mechanism of action, efficacy, cost, and safety in recent years.

Learning disabilities (LDs) have an estimated prevalence between 5% and 9% in the pediatric population and are associated with difficulties in reading, arithmetic, and writing. Previous electroencephalography (EEG) research has reported a lag in alpha-band development in specific LD phenotypes, which seems to offer a possible explanation for differences in EEG maturation. In this study, 40 adolescents aged 10–15 years with LDs underwent 10 sessions of Live Z-Score Training Neurofeedback (LZT-NF) Training to improve their cognition and behavior. Based on the individual alpha peak frequency (i-APF) values from the spectrogram, a group with normal i-APF (ni-APF) and a group with low i-APF (li-APF) were compared in a pre-and-post-LZT-NF intervention. There were no statistical differences in age, gender, or the distribution of LDs between the groups. The li-APF group showed a higher theta absolute power in P4 (p = 0.016) at baseline and higher Hi-Beta absolute power in F3 (p = 0.007) post-treatment compared with the ni-APF group. In both groups, extreme waves (absolute Z-score of ≥1.5) were more likely to move toward the normative values, with better results in the ni-APF group. Conversely, the waves within the normal range at baseline were more likely to move out of the range after treatment in the li-APF group. Our results provide evidence of a viable biomarker for identifying optimal responders for the LZT-NF technique based on the i-APF metric reflecting the patient’s neurophysiological individuality.

Introduction: Inflammatory biomarkers, including Interleukin-6 (IL-6) and Tumor Necrosis Factor-alpha (TNF-alpha), play a significant role in influencing stroke outcomes, particularly in the progression of post-stroke disability. While numerous studies have suggested a correlation between elevated levels of these cytokines and poor functional recovery, further investigation is needed to understand their prognostic value in acute ischemic stroke. Materials and Methods: We conducted a prospective study on 56 patients diagnosed with acute ischemic stroke, evaluating IL-6 and TNF-alpha levels on days 1 and 7 post symptom onset. Stroke severity was assessed using the National Institutes of Health Stroke Scale (NIHSS) and functional outcomes were measured with the modified Rankin Scale (mRS). Statistical analyses were performed to evaluate the association between biomarker levels and stroke severity and recovery. Results: Our study demonstrated that elevated levels of IL-6 and TNF-alpha on both days 1 and 7 were significantly correlated with greater stroke severity and poorer functional outcomes, as indicated by higher NIHSS and mRS scores. These findings are consistent with broader research indicating strong associations between inflammatory cytokines and post-stroke disability, further reinforcing their relevance as prognostic indicators. Conclusions: IL-6 and TNF-alpha are promising biomarkers for predicting stroke severity and functional recovery in acute ischemic stroke. Monitoring these cytokines in the early stages of stroke could aid in identifying patients at higher risk for long-term disability, potentially guiding personalized therapeutic strategies. Further research into anti-inflammatory therapies targeting these cytokines may improve stroke rehabilitation and outcomes.

Gastric cancer stands as the fourth leading cause of cancer-related deaths globally, primarily comprising adenocarcinomas, categorized by anatomic location and histologic type. Often diagnosed at advanced stages, gastric cancer prognosis remains poor. To address the critical need for accurate tumoral markers for gastric cancer diagnosis, we conducted a study to assess classical markers like CEA and CA-19-9 alongside the novel marker miR-106. Our investigation revealed distinct dynamics of these markers compared to non-cancerous groups, although no disparities were observed across different disease stages. Univariable and multivariable logistic regression analyses demonstrated that elevated levels of miR-106, CEA and CA 19-9 were predictive of a positive histopathological exam, with the respective odds ratios of 12.032 (95% CI: 1.948–74.305), 30 (95% CI: 3.141–286.576), and 55.866 (95% CI: 4.512–691.687). Subsequently, we utilized predicted probabilities from regression models to construct receiver operating characteristic (ROC) curves, identifying CA 19-9 as the optimal predictor for gastric adenocarcinoma diagnosis when considering age and gender, with an area under the curve (AUC) of 0.936 (p < 0.001). Hence, classical markers exhibit superior performance compared to the novel marker miR-106 in predicting gastric adenocarcinoma.

Neonatal brain injury or neonatal encephalopathy (NE) is a significant morbidity and mortality factor in preterm and full-term newborns. NE has an incidence in the range of 2.5 to 3.5 per 1000 live births carrying a considerable burden for neurological outcomes such as epilepsy, cerebral palsy, cognitive impairments, and hydrocephaly. Many scoring systems based on different risk factor combinations in regression models have been proposed to predict abnormal outcomes. Birthweight, gestational age, Apgar scores, pH, ultrasound and MRI biomarkers, seizures onset, EEG pattern, and seizure duration were the most referred predictors in the literature. Our study proposes a decision-tree approach based on clinical risk factors for abnormal outcomes in newborns with the neurological syndrome to assist in neonatal encephalopathy prognosis as a complementary tool to the acknowledged scoring systems. We retrospectively studied 188 newborns with associated encephalopathy and seizures in the perinatal period. Etiology and abnormal outcomes were assessed through correlations with the risk factors. We computed mean, median, odds ratios values for birth weight, gestational age, 1-min Apgar Score, 5-min Apgar score, seizures onset, and seizures duration monitoring, applying standard statistical methods first. Subsequently, CART (classification and regression trees) and cluster analysis were employed, further adjusting the medians. Out of 188 cases, 84 were associated to abnormal outcomes. The hierarchy on etiology frequencies was dominated by cerebrovascular impairments, metabolic anomalies, and infections. Both preterms and full-terms at risk were bundled in specific categories defined as high-risk 75–100%, intermediate risk 52.9%, and low risk 0–25% after CART algorithm implementation. Cluster analysis illustrated the median values, profiling at a glance the preterm model in high-risk groups and a full-term model in the inter-mediate-risk category. Our study illustrates that, in addition to standard statistics methodologies, decision-tree approaches could provide a first-step tool for the prognosis of the abnormal outcome in newborns with encephalopathy.