Deep Learning for Combating Misinformation in Multicategorical Text Contents.

Currently, one can observe the evolution of social media networks. In particular, humans are faced with the fact that, often, the opinion of an expert is as important and significant as the opinion of a non-expert. It is possible to observe changes and processes in traditional media that reduce the role of a conventional 'editorial office', placing gradual emphasis on the remote work of journalists and forcing increasingly frequent use of online sources rather than actual reporting work. As a result, social media has become an element of state security, as disinformation and fake news produced by malicious actors can manipulate readers, creating unnecessary debate on topics organically irrelevant to society. This causes a cascading effect, fear of citizens, and eventually threats to the state's security. Advanced data sensors and deep machine learning methods have great potential to enable the creation of effective tools for combating the fake news problem. However, these solutions often need better model generalization in the real world due to data deficits. In this paper, we propose an innovative solution involving a committee of classifiers in order to tackle the fake news detection challenge. In that regard, we introduce a diverse set of base models, each independently trained on sub-corpora with unique characteristics. In particular, we use multi-label text category classification, which helps formulate an ensemble. The experiments were conducted on six different benchmark datasets. The results are promising and open the field for further research.

A Systematic Review of Recommender Systems and Their Applications in Cybersecurity.

This paper discusses the valuable role recommender systems may play in cybersecurity. First, a comprehensive presentation of recommender system types is presented, as well as their advantages and disadvantages, possible applications and security concerns. Then, the paper collects and presents the state of the art concerning the use of recommender systems in cybersecurity; both the existing solutions and future ideas are presented. The contribution of this paper is two-fold: to date, to the best of our knowledge, there has been no work collecting the applications of recommenders for cybersecurity. Moreover, this paper attempts to complete a comprehensive survey of recommender types, after noticing that other works usually mention two-three types at once and neglect the others.

A $10 million question and other cybersecurity-related ethical dilemmas amid the COVID-19 pandemic.

Cybercrime and cybersecurity are like two sides of the same coin: They are opposites but cannot exist without each other. Their mutual relation generates a myriad of ethical issues, ranging from minor to vital. The rapid development of technology will surely involve even more ethical concerns, like the infamous example of a fitness tracking company allegedly paying $10 million worth of ransom. Every cybersecurity solution, tool, or practice has to be ethical by design if it is to protect people and their rights. To identify the ethical issues that cybersecurity/cybercrime might bring about in the future, we conducted the first broad and comprehensive horizon-scanning study since the COVID-19 pandemic arose. As we began this project, nobody had the slightest idea that the coming months would bring the COVID-19 pandemic, and that the reality we had known was about to change dramatically. As it soon became apparent, the deadly coronavirus brought completely new cybersecurity/cybercrime ethical dilemmas to light, and some of the ones known before were transformed or shifted. This article presents the results of our horizon-scanning study concerning the ethical dilemmas that emerged amid the COVID-19 pandemic.

Can the blood tryptase be an indicator of the severity of atopic dermatitis?

Atopic dermatitis (AD - atopic eczema) is a chronic inflammatory dermatosis resulting from complex genetic, epigenetic and environmental interactions with an overlapping defect in the epidermal barrier.AD is one of the most common inflammatory dermatoses in children and adults. AIM: The aim of the study was to assess the relationship between serum basal tryptase (sBT) and total IgE (tIgE) level in blood serum and the severity of lesions (SCORAD; SCORing atopic dermatitis). MATERIALS AND METHODS: The study was performed in the group of adult patients (57 people, F/M: 30/27; average age: 37.5 years) and in the control group (10 people, K/M: 6/4; average age: 44 years). Diagnosis of atopic dermatitis was established by a dermatologist-allergist specialist based on the criteria of Hanifin and Rajka. The severity of lesions was determined on the SCORAD scale (SCORing atopic dermatitis). RESULTS: The distribution of tryptase concentration did not differ statistically significantly between patients with various disease severity and the control group also the severity of skin lesions was significantly higher (p<0.001) in patients whose tIgE level exceeded 3500 IU / ml. Conclusion. sBT did not prove to be a useful biomarker in assessing. CONCLUSIONS: sBT did not prove to be a useful biomarker in assessing severity of AD. The present study demonstrated that in the patients with atopic dermatitis the concentration of total IgE was correlated with severity of the disease symptoms.

Optic nerve disc veins inflammation (papillophlebitis) - case report.

Papillophlebitis is an uncommon disease in clinical practice. We would like to present a case of a 29-year-old patient with atypical orbital pain and flashings, presenting relative afferent pupillary defect and already typical of the disease entity: ophthalmoscopic picture of the fundus and big blind spot in perimetry. We present a complex and interdisciplinary diagnostic process that excludes general diseases such as hypertension, diabetes, coagulation disorders and neurological causes. We leave the only identifiable abnormality and potential source in the inflammatory process of periodontal inflammation and sinus jaw changes. We also describe the process of remitting the changes and finally a favorable end result of the primarily a very disturbing clinical picture that this disease may present.

Methodological approach for trace and essential elements assessment in prostate tissue by SRIXE method.

OBJECTIVE: The goal of this contribution is to present and familiarize the medical community with the method for the assessment of trace and essentials elements in prostate tissue sections. MATERIALS AND METHODS: X-ray fluorescence based technique(namely Synchrotron Induced X-ray Emission (SRIXE)) is described in terms of methodology, sample preparation and the evaluation of the recorded results (spectral data sets). Materials for the samples were collected from the patients underwent radical prostatectomy due to Adenocarcinoma prostatae. Specimens were freeze-dried, cut by microtome (to the thickness of 15 µm), one slice was placed on Mylar foil (for SRIXE measurements) and adjacent one on microscopic glass (for histopathological assessment). RESULTS: Results presented here show the usability of SRIXE method for the evaluation of concentration of trace and essential elements in prostate tissue sections with the spatial resolution better than 15 microns. DISCUSSION: Histopathological analysis of samples, which is only focused on morphological features, is unable to reveal information about changes in biochemical signature of tissues affected by the illness. SRIXE is a powerful and promising technique to analyse even very low concentrations oat the cellular level without any labelling or separating procedures. Obtained results may be correlated with classic histopathological assessment allowing for drawing conclusions on the changes in certain elements concentrations with the progression of disease. Moreover, mentioned in this work analysis, can be performed for any type of biological tissues.

Correction: Conflicting 'mother-scientist' roles. An innovative application of basket analysis in social research.

[This corrects the article DOI: 10.1371/journal.pone.0276201.].

Acute Ischaemic Stroke in Patients Treated with Direct Oral Anticoagulants: Potential Causes, Clinical Characteristics, and Short-Term Outcomes.

Introduction: Direct oral anticoagulants (DOAC) are the first-line treatment for primary and secondary acute ischaemic stroke (AIS) prevention in patients with nonvalvular atrial fibrillation (NVAF), but a significant percentage of patients develop AIS despite being treated with DOAC. As the number of DOAC-treated patients is growing, so is the number of patients with AIS on DOAC. The aim of the study was to assess the incidence of AIS with prestroke DOAC treatment among patients hospitalised in the University Hospital in Kraków, to analyse the clinical characteristics of AIS occurring in patients on DOAC, and to identify potential causes of treatment ineffectiveness in this group. Materials and Methods: In the study, we included all patients hospitalised in the Department of Neurology of the University Hospital in Kraków within one year (July 2022 to June 2023) with the diagnosis of AIS. The group was divided into two subgroups of patients with and without prestroke DOAC treatment. Based on medical files, we retrospectively analysed the profile of cardiovascular risk factors, stroke severity (assessed with National Institutes of Health Stroke Scale, NIHSS), use of causative stroke treatment and short-term outcomes (defined as NIHSS score, modified Rankin scale (mRS) score at discharge, in-hospital mortality, and secondary intracerebral haemorrhage among patients treated with mechanical thrombectomy, MT). Within the DOAC-treated subgroup, we looked for potential causes of AIS occurring despite DOAC treatment (valvular AF, poor adherence to treatment, underdosing, other prothrombotic conditions, aetiology of stroke other than thromboembolic, and drug-drug interactions). Results: In the study, we included 768 AIS patients. 109 (14.2%) had a history of prestroke DOAC treatment. A potential cause of DOAC treatment failure was identified in the majority of them (n = 63, 57.8%). Patients with prestroke DOAC treatment had worse functional condition before stroke and higher stroke severity on admission but similar short-term outcomes and similar short-term effects of treatment with MT. DOAC (+) and DOAC (-) patients had different profiles of cardiovascular risk factors and different factors associated with short-term outcome. Conclusions and Clinical Implications. A potential cause of AIS occurring in DOAC-treated patients can be identified in most cases and in many of them prevented.

Conflicting 'mother-scientist' roles. An innovative application of basket analysis in social research.

The article discusses the issue of career and motherhood of female scientists, which is part of a broader thematic area known as Work-Life Balance. The theoretical part refers to the social role theory and the mutual influence of work and career on family and motherhood. The situation of women scientists is presented, for whom fulfilling the role of a mother is an important, although natural barrier on the road to a scientific career. Previous analyses present in the literature revealed that for the vast majority of mothers-scientists, motherhood is a factor that significantly delays their plans related to the development of a scientific career. The paper presents the results of empirical research conducted on the basis of classical academic methodology. Then, based on the data obtained from 334 mothers-scientists, an innovative, multidisciplinary experiment using data mining solutions was conducted, to answer the research question: Is the basket analysis tool able to find possible correlations between the factors characterising the respondents, and the types and dimensions of conflict occurring between scientific career and motherhood they experience? The paper shows that, according to the study results, most respondents declare they indeed experience the conflict between the roles of a mother and a scientist. The most frequently declared dimension of the conflict is the time-related one, then subsequently the emotional dimension, and lastly the financial dimension; many scientists declare they experience more than one dimension of conflict. Lastly, the basket analysis tool objectively confirmed the occurrence of correlations between the factors characterising the respondents and the types and dimensions of conflict occurring between scientific career and motherhood.

Opening up a discussion on the native speaker myth in science.

The commentary touches upon the topic which is relevant to hundreds of thousands of researchers in the world. When trying to publish in English, they are often advised to ask the native speakers of the language for their opinion. However, as English has become the international, cross-border language of science, it may have ceased to be the property of the native speaker researchers, who constitute a small minority in the community. In addition, when English is used as a lingua franca, it is the message which counts, not the particular style or spelling. The commentary finishes with an appeal not to hinder the development of science by slowing down the editing process, and thus not to close the door for diversity and new perspectives.

Development and Psychometric Properties of the Caregiver Burden Scale in Polish Caregivers of Stroke Patients.

Purpose: Most stroke patients require long-term care of their family members. Excessive care burden entails several negative consequences; therefore, the severity of the burden should be periodically assessed. For this purpose, valid multidimensional measures are needed. Our study, which is a part of a larger research project, aims to translate and evaluate the psychometric properties of the Polish Caregiver Burden Scale (CBS) in relation to construct validity and internal consistency in caregivers of the patients after stroke. Patients and Methods: The sample of this cross-sectional observational study consisted of 366 informal caregivers to consecutive first-ever stroke survivors. The five-factor Polish CBS and the Hospital Anxiety and Depression Scale (HADS) were administered during the home visits at three to six months after patients' hospitalisation. Exploratory (EFA), confirmatory (CFA) factor analyses and a net analysis were performed to investigate the internal structure and a factorial construct validity of the CBS. Correlation analyses between the CBS and the HADS were carried out to examine convergent validity. Cronbach's alpha and item-total correlation were applied to assess internal consistency. Results: Three out of five factors identified by EFA were similar to the original indices of the CBS, while the remaining two deviated from the original structure of the CBS. The CFA five-factor model represented an acceptable fit (confirmatory fit index, CFI = 0.96, root mean square error, RMSEA = 0.04) but only after a modification. All subscale scores of the CBS were positively correlated with the HADS, supporting the convergent validity. Cronbach's alpha coefficients for the overall scale (0.92) and all subscales (0.72-0.87) except one (0.69) and item-total correlation results indicated good internal consistency. Conclusion: The Polish version of the CBS showed acceptable internal consistency and good convergent validity. Factorial validity and structural integrity were partially supported. The interrelationships between the CBS subdomains, their partial mutual contamination, and the scale's non-orthogonal structure should be considered when interpreting the results of further studies using this version of the scale.

Has the pandemic made us more digitally literate?: Innovative association rule mining study of the relationships between shifts in digital skills and cybersecurity awareness occurring whilst working remotely during the COVID-19 pandemic.

Digital literacy has been included in the set of the eight key competences, which are necessary to enjoy life to the full in the twenty-first century. According to the previous studies, women tend to possess lower digital competence than men; the older the person, the lower the level of digital literacy. To date, Polish citizens in general have worse skills than the European average. This may lead to people being socially excluded and vulnerable to cybersecurity threats, especially in the times of the COVID-19 pandemic, which requires them to work, study and shop using the Internet. The study concerned Polish women who work at universities, as scientists and teachers. Their perceived level of their digital literacy has been studied in the broad campaign, along with their awareness of the cybersecurity matters. Then, the collected results were processed with an association rules mining algorithm, uncovering the factors related to the shifts in them.

Innovative machine learning approach and evaluation campaign for predicting the subjective feeling of work-life balance among employees.

At present, many researchers see hope that artificial intelligence, machine learning in particular, will improve several aspects of the everyday life for individuals, cities and whole nations alike. For example, it has been speculated that the so-called machine learning could soon relieve employees of part of the duties, which may improve processes or help to find the most effective ways of performing tasks. Consequently, in the long run, it would help to enhance employees' work-life balance. Thus, workers' overall quality of life would improve, too. However, what would happen if machine learning as such were employed to try and find the ways of achieving work-life balance? This is why the authors of the paper decided to utilize a machine learning tool to search for the factors that influence the subjective feeling of one's work-life balance. The possible results could help to predict and prevent the occurrence of work-life imbalance in the future. In order to do so, the data provided by an exceptionally sizeable group of 800 employees was utilised; it was one of the largest sample groups in similar studies in Poland so far. Additionally, this was one of the first studies where so many employees had been analysed using an artificial neural network. In order to enable replicability of the study, the specific setup of the study and the description of the dataset are provided. Having analysed the data and having conducted several experiments, the correlations between some factors and work-life balance have indeed been identified: it has been found that the most significant was the relation between the feeling of balance and the actual working hours; shifting it resulted in the tool predicting the switch from balance to imbalance, and vice versa. Other factors that proved significant for the predicted WLB are the amount of free time a week the employee has for themselves, working at weekends only, being self-employed and the subjective assessment of one's financial status. In the study the dataset gets balanced, the most important features are selected with the selectKbest algorithm, an artificial neural network of 2 hidden layers with 50 and 25 neurons, ReLU and ADAM is constructed and trained on 90% of the dataset. In tests, it predicts WLB based on the prepared dataset and selected features with 81% accuracy.