Applying Deep Neural Networks for Predicting Dark Triad Personality Trait of Online Users

© 2020 IEEE. In the recent times, the social networking sites act as a rich source of information, which is shared among online users, who post comments and express their opinions in the form of likes and dislikes. Such content reflects important clues about the personality and behavior of the online community. The dark triad personality traits, such as the psychopathic behavior of individuals, can be detected using computational models. The earlier studies on the dark triad (psychopath) prediction exploit traditional machine learning techniques with limited dataset size. Therefore, it is required to develop an advanced deep neural network-based technique. In this work, we implement a deep neural network model, namely BILSTM for the efficient prediction of dark triad (psychopath) personality traits regarding online users. Experimental results depict that the proposed model attained an improved AUC (0.82) when compared to the baseline study

Spatiotemporal Analysis of Web News Archives for Crime Prediction

In today's world, security is the most prominent aspect which has been given higher priority. Despite the rapid growth and usage of digital devices, lucrative measurement of crimes in under-developing countries is still challenging. In this work, unstructural crime data (900 records) from the news archives of the previous eight years were extracted to predict the behavior of criminals' networks and transform it into useful information using natural language processing (NLP). To estimate the next move of criminals in Pakistan, we performed hotspot-based spatial analysis. Later, this information is fed to two different classifiers for possible identification and prediction. We achieved the maximum accuracy of 92% using K-Nearest Neighbor (KNN) and 62% using the Random Forest algorithm. In terms of crimes, the results showed that the most prevalent crime events are robberies. Thus, the usage of digital information archives, spatial analysis, and machine learning techniques can open new ways of handling a peaceful and sustainable society in eradicating crimes for countries having paucity of financial resources

Aetiology and Outcome of Childhood Convulsive Status Epilepticus: A tertiary care experience in Oman

Objective: This study aimed to evaluate the etiology, management, and outcomes of convulsive status epilepticus (CSE) in children highlighting the factors that affect patient outcome. Methods: In a retrospective study spanning 2020 to 2023, 93 children with convulsive status epilepticus (CSE) treated at Sultan Qaboos University Hospital's emergency department (ED), High Dependency (HD), and intensive care unit (ICU) were analyzed. The Modified Rankin Scale at discharge determined CSE outcome. Results: Study of 93 children (mean age 4.84 years ± 3.64), predominantly Omani (92.47%). Acute 14 symptomatic (37.7%) and  febrile tatus (31.2%) were primary causes. Diazepam used in 67.44% 15 cases as first-line treatment, with median seizure duration of 45 minutes. Successful control achieved in 16 76.34% within 60 minutes. Return to baseline in 55.9%, 5.38% mortality, and 38.7% disability. Etiology and 17 duration significantly impacted outcomes (p < 0.05). Conclusion: Acute symptomatic is the most common etiology of CSE, and a longer duration is associated with higher mortality and neurological disability. Therefore, managing CSE promptly and appropriately is crucial. Furthermore, identifying and treating the underlying cause is essential to reduce the duration of CSE and improve the outcome. Keywords: Etiology, Outcome, Convulsive Status Epilepticus, Modified Rankin Scal

Knowledge, attitudes and practices survey on organ donation among a selected adult population of Pakistan

Background: To determine the knowledge, attitudes and practices regarding organ donation in a selected adult population in Pakistan. Methods: Convenience sampling was used to generate a sample of 440, 408 interviews were successfully completed and used for analysis. Data collection was carried out via a face to face interview based on a pre-tested questionnaire in selected public areas of Karachi, Pakistan. Data was analyzed using SPSS v. 15 and associations were tested using the Pearson\u27s Chi square test. Multiple logistic regression was used to find independent predictors of knowledge status and motivation of organ donation. Results: Knowledge about organ donation was significantly associated with education (p = 0.000) and socioeconomic status (p = 0.038). 70/198 (35.3%) people expressed a high motivation to donate. Allowance of organ donation in religion was significantly associated with the motivation to donate (p = 0.000). Multiple logistic regression analysis revealed that higher level of education and higher socioeconomic status were significant (p \u3c 0.05) independent predictors of knowledge status of organ donation. For motivation, multiple logistic regression revealed that higher socioeconomic status, adequate knowledge score and belief that organ donation is allowed in religion were significant (p \u3c 0.05) independent predictors. Television emerged as the major source of information. Only 3.5% had themselves donated an organ, with only one person being an actual kidney donor. Conclusion: Better knowledge may ultimately translate into the act of donation. Effective measures should be taken to educate people with relevant information with the involvement of media, doctors and religious scholars

Effect of N′-nitrosodimethylamine on red blood cell rheology and proteomic profiles of brain in male albino rats

We investigated the effects of N'-nitrosodimethylamine (NDMA) induced toxicity on red blood cell rheology in male rats and identified bands in proteomic profiles of brain which can be used as novel markers. Polyacrylamide gel electrophoresis (PAGE) profiles exhibited constitutive as well as induced expression of the polypeptides. Remarkably, the molecular weight range of the polypeptides (8–150 kDa) corresponded to that of the family of heat shock proteins. Our results revealed significant changes in blood parameters and showed the presence of acanthocytes, tear drop cells, spicules and cobot rings in the treated categories. Lactate dehydrogenase and esterase zymograms displayed a shift to anaerobic metabolism generating hypoxia-like conditions. This study strongly suggests that NDMA treatment causes acute toxicity leading to cell membrane destruction and alters protein profiles in rats. It is therefore recommended that caution should be exercised in using NDMA to avoid risks, and if at all necessary strategies should be designed to combat such conditions

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Kartagener’s Syndrome Complicated by Bronchiectasis with Tricuspid and Mitral Valve Regurgitation: A Case Report

Background: Kartagener’s syndrome, a rare autosomal recessive genetic disorder, is characterized by primary ciliary dyskinesia (PCD), resulting in defective cilia function in the respiratory tract and fallopian tubes. Case presentation: This case report discusses a 23-year-old female with Kartagener’s syndrome, bronchiectasis, and cardiac involvement, who presented with shortness of breath, cough, and syncope. Notably, she received home oxygen therapy but became exhausted, leading to loss of consciousness. Clinical examination revealed prominent heart sounds and abnormal lung findings. Laboratory results indicated leukocytosis, and an ECG confirmed dextrocardia and cardiac abnormalities. Doppler studies identified mitral and tricuspid regurgitation along with severe pulmonary arterial hypertension. Antibiotics were administered for coagulase-negative Staphylococcus infection. The patient improved with a treatment regimen, including oxygenation and nebulization. Regular follow-up and patient education were emphasized. Conclusion: This case underscores the complexity of Kartagener’s syndrome and the importance of a multidisciplinary approach in managing its respiratory and cardiac manifestations

Spatiotemporal Analysis of Web News Archives for Crime Prediction

In today’s world, security is the most prominent aspect which has been given higher priority. Despite the rapid growth and usage of digital devices, lucrative measurement of crimes in under-developing countries is still challenging. In this work, unstructural crime data (900 records) from the news archives of the previous eight years were extracted to predict the behavior of criminals’ networks and transform it into useful information using natural language processing (NLP). To estimate the next move of criminals in Pakistan, we performed hotspot-based spatial analysis. Later, this information is fed to two different classifiers for possible identification and prediction. We achieved the maximum accuracy of 92% using K-Nearest Neighbor (KNN) and 62% using the Random Forest algorithm. In terms of crimes, the results showed that the most prevalent crime events are robberies. Thus, the usage of digital information archives, spatial analysis, and machine learning techniques can open new ways of handling a peaceful and sustainable society in eradicating crimes for countries having paucity of financial resources