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This study aims to examine the motivational factors that direct individuals to computer games in the process of evaluating leisure activities. The study is designed in descriptive and relational survey models, which are among the quantitative research patterns. A total of 1677 individuals participated in the study. A personal information form and the Computer Gaming Motivation Scale were used. An independent sample t-test, one-way analysis of variance, and Pearson correlation test were used to analyze the data. According to the results, there were significant differences in the concentration, entertainment, escape, learning, and socialization sub-dimensions of the participants in favor of the groups playing in Internet cafes. The findings indicate that when more time is spent with information and communication technologies, there is a decrease in concentration and an increase in entertainment and escape. Furthermore, when the relationship between the sub-dimensions of the scale was examined, positively moderate and high correlations were found among concentration, entertainment, escape, learning, and socialization. As a result, rapid technological changes and developments provide many opportunities for individuals. It is thought that individuals’ busy work lives and monotonous daily routines cause them to prefer easily accessible activities during their leisure time.

Unsupervised anomaly detection for spatio-temporal data has extensive use in a wide variety of applications such as earth science, traffic monitoring, fraud and disease outbreak detection. Most real-world time series data have a spatial dimension as an additional context which is often expressed in terms of coordinates of the region of interest (such as latitude - longitude information). However, existing techniques are limited to handle spatial and temporal contextual attributes in an integrated and meaningful way considering both spatial and temporal dependency between observations. In this paper, a hybrid deep learning framework is proposed to solve the unsupervised anomaly detection problem in multivariate spatio-temporal data. The proposed framework works with unlabeled data and no prior knowledge about anomalies are assumed. As a case study, we use the public COVID-19 data provided by the Italian Department of Civil Protection. Northern Italy regions' COVID-19 data are used to train the framework; and then any abnormal trends or upswings in COVID-19 data of central and southern Italian regions are detected. The proposed framework detects early signals of the COVID-19 outbreak in test regions based on the reconstruction error. For performance comparison, we perform a detailed evaluation of 15 algorithms on the COVID-19 Italy dataset including the state-of-the-art deep learning architectures. Experimental results show that our framework shows significant improvement on unsupervised anomaly detection performance even in data scarce and high contamination ratio scenarios (where the ratio of anomalies in the data set is more than 5%). It achieves the earliest detection of COVID-19 outbreak and shows better performance on tracking the peaks of the COVID-19 pandemic in test regions. As the timeliness of detection is quite important in the fight against any outbreak, our framework provides useful insight to suppress the resurgence of local novel coronavirus outbreaks as early as possible.

Background Neurological COVID-19 disease has been reported widely, but published studies often lack information on neurological outcomes and prognostic risk factors. We aimed to describe the spectrum of neurological disease in hospitalised COVID-19 patients; characterise clinical outcomes; and investigate factors associated with a poor outcome. Methods We conducted an individual patient data (IPD) meta-analysis of hospitalised patients with neurological COVID-19 disease, using standard case definitions. We invited authors of studies from the first pandemic wave, plus clinicians in the Global COVID-Neuro Network with unpublished data, to contribute. We analysed features associated with poor outcome (moderate to severe disability or death, 3 to 6 on the modified Rankin Scale) using multivariable models. Results We included 83 studies (31 unpublished) providing IPD for 1979 patients with COVID-19 and acute new-onset neurological disease. Encephalopathy (978 [49%] patients) and cerebrovascular events (506 [26%]) were the most common diagnoses. Respiratory and systemic symptoms preceded neurological features in 93% of patients; one third developed neurological disease after hospital admission. A poor outcome was more common in patients with cerebrovascular events (76% [95% CI 67–82]), than encephalopathy (54% [42–65]). Intensive care use was high (38% [35–41]) overall, and also greater in the cerebrovascular patients. In the cerebrovascular, but not encephalopathic patients, risk factors for poor outcome included breathlessness on admission and elevated D-dimer. Overall, 30-day mortality was 30% [27–32]. The hazard of death was comparatively lower for patients in the WHO European region. Interpretation Neurological COVID-19 disease poses a considerable burden in terms of disease outcomes and use of hospital resources from prolonged intensive care and inpatient admission; preliminary data suggest these may differ according to WHO regions and country income levels. The different risk factors for encephalopathy and stroke suggest different disease mechanisms which may be amenable to intervention, especially in those who develop neurological symptoms after hospital admission.

Abdurrahman Kaya,1 Sibel Yıldız Kaya2 1Department of Infectious Disease, Istanbul Training and Research Hospital, Istanbul, Turkey; 2Department of Infectious Disease, Sungurlu State Hospital, Istanbul, TurkeyCorrespondence: Abdurrahman KayaDepartment of Infectious Disease, Istanbul Training and Research Hospital, Istanbul, TurkeyTel +90 506 6113328Email dr.abdkaya@hotmail.com View the original paper by Dr Lensen and colleagues

Abstract Background Rapid detection of sources and transmission routes by molecular methods provides key data for risk management of methicillin-resistant Staphylococcus aureus-induced infections acquired in both the community and hospitals. This study aimed to determine the clonal relationship of methicillin-resistant S. aureus strains isolated from our hospital by pulsed-field gel electrophoresis (PFGE) and Staphylococcal protein A (spa) typing methods and to identify the predominant clones in Cukurova Region, Turkey. Results All isolates analyzed by PFGE were distributed among 11 clusters. Clusters A (n = 19) and B (n = 27) were 84.1% similar and accounted for 61% of all samples. All isolates were distributed among 18 spa types, with the most common type being t030 with 31 isolates (41.3%), followed by t223 with nine isolates (12%) and t127 with seven isolates (9.3%). Conclusions We found that t030 was the most common spa type in the area where the study was conducted, as also previously shown in studies undertaken in Turkey. However, the rate of t030 in our study was below the rates reported in the literature. We also detected some rare or sporadic spa types like t127, which has not been previously defined in our country. We consider that the spa typing and PFGE methods are useful for research on clonal relations in monitoring the changing prevalent clones in specific regions.

Multivariate time-series data with a contextual spatial attribute have extensive use for finding anomalous patterns in a wide variety of application domains such as earth science, hurricane tracking, fraud, and disease outbreak detection. In most settings, spatial context is often expressed in terms of ZIP code or region coordinates such as latitude and longitude. However, traditional anomaly detection techniques cannot handle more than one contextual attribute in a unified way. In this paper, a new hybrid approach based on deep learning is proposed to solve the anomaly detection problem in multivariate spatio-temporal dataset. It works under the assumption that no prior knowledge about the dataset and anomalies are available. The architecture of the proposed hybrid framework is based on an autoencoder scheme, and it is more efficient in extracting features from the spatio-temporal multivariate datasets compared to the traditional spatio-temporal anomaly detection techniques. We conducted extensive experiments using buoy data of 2005 from National Data Buoy Center and Hurricane Katrina as ground truth. Experiments demonstrate that the proposed model achieves more than 10% improvement in accuracy over the methods used in the comparison where our model jointly processes the spatial and temporal dimensions of the contextual data to extract features for anomaly detection.

OBJECTIVE: Migraine is an independent risk factor for ischemic stroke, but its pathophysiology is still unclear. Genetic factors that predispose patients to thrombosis have been studied in patients with migraine to highlight the pathogenesis, but the results remain controversial. In this study, the frequencies of factor V Leiden (FVL), prothrombin (Pt) G20210A and methylenetetrahydrofolate reductase (MTHFR) C677T mutations were investigated. METHODS: One hundred and sixty patients aged of 15 to 55 years with no history of systemic disease and who had been diagnosed as migraine according to the International Headache Society (IHS) diagnostic criteria at Baskent University Hospital Neurology Outpatient Clinics were investigated for FVL, Pt G20210A and MTHFR C677T mutations from their genomic DNA, and the results were compared with those of healthy controls. RESULTS: One hundred and fifty five (96.9%) of 160 migraine patients were homozygote normal, 5 (3.1%) were heterozygote and none of them were homozygote mutant for FVL. The control group had 9.8% heterozygote individuals but the difference between the percentages was not statistically significant (p> 0.05). There were no homozygote mutant individuals in the Turkish population study in normal subjects like our study. Thirty nine (24.4%) of 160 migraine patients were heterozygote and 8 (5%) were homozygote mutant for MTHFR C677T. The control group had 37 (34.9%) heterozygote and 6 (5.6%) homozygote mutant individuals. The difference between the percentages was not statistically significant (p= 0.15). Three (1.9%) of 160 migraine patients were heterozygote and 5 (2.9%) of the control group were heterozygote mutant for Pt G20210A mutation. The control group had 37 (34.9%) heterozygote and 6 (5.6%) homozygote mutant individuals. The difference between the percentages was not statistically significant (p= 0.420). CONCLUSION: Our study indicates that FVL, Pt G20210A and MTHFR C677T gene mutations, which are considered as risk factors for thrombosis, were not found to be associated with migraine pathogenesis in our population. The differences between populations probably depend on variations in ethnic origin.

In this work local features are used in feature extraction process in image processing for textures. The local binary pattern feature extraction method from textures are introduced. Filtering is also used during the feature extraction process for getting discriminative features. To show the effectiveness of the algorithm before the extraction process, three different noise are added to both train and test images. Wiener filter and median filter are used to remove the noise from images. We evaluate the performance of the method with Naïve Bayesian classifier. We conduct the comparative analysis on benchmark dataset with different filtering and size. Our experiments demonstrate that feature extraction process combine with filtering give promising results on noisy images.

SUMMARY Leptospirosis is still one of the most important health problems in developing countries located in humid tropical and subtropical regions. Human infections are generally caused by exposure to water, soil or food contaminated with the urine of infected wild and domestic animals such as rodents and dogs. The clinical course of leptospirosis is variable and may be difficult to distinguish from many other infectious diseases. The dark-field microscopy (DFM), serology and nucleic acid amplification techniques are used to diagnose leptospirosis, however, a distinctive standard reference method is still lacking. Therefore, in this study, we aimed to determine the presence of Leptospira spp., to differentiate the pathogenic L. interrogans and the non-pathogenic L. biflexa, and also to determine the sensitivity and specificity values of molecular methods as an alternative to conventional ones. A total of 133 serum samples, from 47 humans and 86 cattle were evaluated by two conventional tests: the Microagglutination Test (MAT) and the DFM, as well as three molecular methods, the 16S rRNA-PCR followed by Restriction Fragment Lenght Polymorphism (RFLP) of the amplification products 16S rRNA-PCR-RFLP, LipL32-PCR and OmpL1-PCR. In this study, for L. interrogans, the specificity and sensitivity rates of the 16S rRNA-PCR and the LipL32-PCR were considered similar (100% versus 98.25% and 100% versus 98.68%, respectively). The OmpL1-PCR was able to classify L. interrogans into two intergroups, but this PCR was less sensitive (87.01%) than the other two PCR methods. The 16S rRNA-PCR-RFLP could detect L. biflexa DNA, but LipL32-PCR and OmpL1-PCR could not. The 16S rRNA-PCR-RFLP provided an early and accurate diagnosis and was able to distinguish pathogenic and non-pathogenic Leptospira species, hence it may be used as an alternative method to the conventional gold standard techniques for the rapid disgnosis of leptospirosis.

Pathergy test indicates nonspecific hyper-reactivity of the skin to aseptic trauma in Behçet syndrome (BS) and is considered as an adjunctive diagnostic test with a good specificity albeit with low sensitivity. We tested the hypothesis that a relationship exists between active clinical manifestations of BS and the pathergy-positivity when performed simultaneously. Pathergy test and detailed dermatologic examination were done in 105 BS patients (60M/45F); who were seen consecutively at the multi-disciplinary BS outpatient clinic in a single tertiary center. Information regarding demographic and clinical characteristics, pathergy test results at diagnosis, and details about treatment were obtained from patient charts. Disease activity was assessed using Behçet Disease Current Activity Form. Among 105 patients, 27 (25.7%) were pathergy-positive at the time of the study visit whereas 40.9% were pathergy-positive at the time of the diagnosis. There was no relation between pathergy test and patient age or disease duration, either. Pathergy-positivity was significantly more common in patients with folliculitis compared to those without folliculitis (40.7% vs 19.2%; P = .026). The test was also positive in all 3 patients with leg ulcers due to venous stasis. We found that among all skin-mucosa lesions only the presence of folliculitis was associated with pathergy positivity with statistical significance. It was also remarkable that the current pathergy was positive in all 3 patients with active leg ulcers but this finding warrants further studies because of the low patient numbers., Competing Interests: Zeynep Altan Ferhatoglu received honoraria, consulting or speaker fees from Eczacibaşi and Novartis. Emire Seyahi has received honoraria, consulting or speaker fees from Novartis, Pfizer, AbbVie and Gilead. All other authors have no funding and conflicts of interest to disclose., (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)

The field of prosthetics has been evolving and advancing over the past decade, as patients with missing extremities are expecting to control their prostheses in as normal a way as possible. Scientists have attempted to satisfy this expectation by designing a connection between the nervous system of the patient and the prosthetic limb, creating the field of neuroprosthetics. In this paper, we broadly review the techniques used to bridge the patient's peripheral nervous system to a prosthetic limb. First, we describe the electrical methods including myoelectric systems, surgical innovations and the role of nerve electrodes. We then describe non-electrical methods used alone or in combination with electrical methods. Design concerns from an engineering point of view are explored, and novel improvements to obtain a more stable interface are described. Finally, a critique of the methods with respect to their long-term impacts is provided. In this review, nerve electrodes are found to be one of the most promising interfaces in the future for intuitive user control. Clinical trials with larger patient populations, and for longer periods of time for certain interfaces, will help to evaluate the clinical application of nerve electrodes.