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Abstract Background Medical education has undergone significant changes over the last decades. Scientific and technological progress alongside contemporary society’s changing requirements have driven demand for highly trained, competent doctors. In response to this need, university faculties of medicine have sought innovative forms of teaching and evaluating the students on their degree courses. The aim of this study was to quantify the characteristics and extent of academic training in teaching methods, of participation in innovative teaching initiatives, and training in simulation and debriefing among the teaching personnel on the degree course in medicine at the University of Granada (Spain). Methods This transversal descriptive study was conducted among a population of 121 educators teaching on the medical degree course at the University of Granada, Spain. All responded to a specially designed CoRe-Content Representation questionnaire. This consisted of various parts: (a) demographic data; (b) teaching experience and qualifications; (c) specific information about training in teaching skills received. The Fisher test was applied whenever the dependent variable had two values (dichotomous) and the Chi-square test when it had more than two values (polytomic). Statistical significance was established with an alpha error of 5%. Results The results showed that 87.60% of the educators had received no training in debriefing. There was a notable gender gap, whereby women held fewer management posts, fewer were engaged in clinical activity, and fewer had undergone training in clinical simulation. Teachers with degrees in medicine had undergone less regulated training than educators with other degree qualifications. Conclusion The main areas of medical training that require improvement (and so present challenges to be met in the years to come) are as follows: a definitive solution to the existing gender gap, general implementation of new educational models and methods (especially learning based on clinical problem-solving and simulation), closing generation gaps, and improved training processes for educators with clinical attachment.

The management of schizophrenia necessitates a comprehensive treatment paradigm that considers individual patient nuances and the efficacy of lurasidone in addressing schizophrenia symptoms, particularly at elevated dosages. Numerous randomized trials have affirmed the efficacy of lurasidone across various dimensions of schizophrenia, demonstrating marked enhancements in positive, negative and cognitive symptoms compared to a placebo. In addition, lurasidone exhibits potential in ameliorating agitation amongst acutely ill patients, showcasing greater efficacy at higher doses. However, despite the favourable outcomes observed with higher lurasidone doses, routine clinical practice often opts for lower doses, potentially limiting its maximal therapeutic impact. Furthermore, lurasidone also shows efficacy in reducing post-psychotic depression in dual psychosis. Moreover, practical insights into lurasidone usage encompass swift dose escalation within a 1–5-day span and recommended combination strategies with other medications such as benzodiazepines for insomnia or agitation, beta-blockers for akathisia, and antihistamines or antimuscarinic drugs for patients transitioning rapidly from antipsychotics with substantial antihistamine and/or anticholinergic effects. Finally, a series of clinical cases is presented, highlighting benefits of lurasidone in terms of cognitive function, functional recovery and other therapeutic aspects for the management of schizophrenia.

In recent years micro- and nanoplastics and metal-oxide nanomaterials have been found in several environmental compartments. The Antarctic soft clam Laternula elliptica is an endemic Antarctic species having a wide distribution in the Southern Ocean. Being a filter-feeder, it could act as suitable bioindicator of pollution from nanoparticles also considering its sensitivity to various sources of stress. The present study aims to assess the impact of polystyrene nanoparticles (PS-NP) and the nanometal titanium-dioxide (n-TiO2) on genome-wide transcript expression of L. elliptica either alone and in combination and at two toxicological relevant concentrations (5 and 50 µg/L) during 96 h exposure. Transcript-target qRT-PCR was performed with the aim to identify suitable biomarkers of exposure and effects. As expected, at the highest concentration tested, the clustering was clearer between control and exposed clams. A total of 221 genes resulted differentially expressed in exposed clams and control ones, and 21 of them had functional annotation such as ribosomal proteins, antioxidant, ion transport (osmoregulation), acid-base balance, immunity, lipid metabolism, cell adhesion, cytoskeleton, apoptosis, chromatin condensation and cell signaling. At functional level, relevant transcripts were shared among some treatments and could be considered as general stress due to nanoparticle exposure. After applying transcript-target approach duplicating the number of clam samples, four ecologically relevant transcripts were revealed as biomarkers for PS-NP, n-TiO2 and their combination at 50 µg/L, that could be used for monitoring clams’ health status in different Antarctic localities.

This study contributes to the Sybil node-detecting algorithm in online social networks (OSNs). As major communication platforms, online social networks are significantly guarded from malicious activity. A thorough literature review identified various detection and prevention Sybil attack algorithms. An additional exploration of distinct reputation systems and their practical applications led to this study’s discovery of machine learning algorithms, i.e., the KNN, support vector machine, and random forest algorithms, as part of our SybilSocNet. This study details the data-cleansing process for the employed dataset for optimizing the computational demands required to train machine learning algorithms, achieved through dataset partitioning. Such a process led to an explanation and analysis of our conducted experiments and comparing their results. The experiments demonstrated the algorithm’s ability to detect Sybil nodes in OSNs (99.9% accuracy in SVM, 99.6% in random forest, and 97% in KNN algorithms), and we propose future research opportunities.