Incidence and Prognosis of COVID-19 in Patients with Psoriasis: A Multicenter Prospective Study from the Eastern Black Sea Region of Turkey

COVID-19 infection can have a poor prognosis, especial- ly in patients with chronic diseases and those receiving immunosup- pressive or immunomodulating therapies. This study aimed to investigate the severity of COVID-19 infection in patients with psoriasis and compare the infection severity for sys- temic treatments and comorbidities. We conducted a study in the dermatology clinics of five different centers in the Eastern Black Sea region of Turkey. Four hundred and eighty-eight patients were included, and 22.5% were confirmed as having COVID-19 infection. In our study, the frequency of hospitalization rates due to COVID-19 infection were similar (15.4%, 25.9% respectively) in patients receiv- ing biological treatment and receiving non-biological systemic treat- ment (P=0.344). Hospitalization rates were higher in patients with hypertension, androgenetic alopecia, and acitretin use (P=0.043, P=0.028, P=0.040). In conclusion, current biologic treatments and non-biologic system- ic treatments in patients with psoriasis did not appear to increase the risk of the severe form of COVID-19, except for acitretin

Incidence and Prognosis of COVID-19 in Patients with Psoriasis: A Multicenter Prospective Study from the Eastern Black Sea Region of Turkey

COVID-19 infection can have a poor prognosis, especial- ly in patients with chronic diseases and those receiving immunosup- pressive or immunomodulating therapies. This study aimed to investigate the severity of COVID-19 infection in patients with psoriasis and compare the infection severity for sys- temic treatments and comorbidities. We conducted a study in the dermatology clinics of five different centers in the Eastern Black Sea region of Turkey. Four hundred and eighty-eight patients were included, and 22.5% were confirmed as having COVID-19 infection. In our study, the frequency of hospitalization rates due to COVID-19 infection were similar (15.4%, 25.9% respectively) in patients receiv- ing biological treatment and receiving non-biological systemic treat- ment (P=0.344). Hospitalization rates were higher in patients with hypertension, androgenetic alopecia, and acitretin use (P=0.043, P=0.028, P=0.040). In conclusion, current biologic treatments and non-biologic system- ic treatments in patients with psoriasis did not appear to increase the risk of the severe form of COVID-19, except for acitretin

Contemporary Challenges and Solutions

CA18131 CP16/00163 NIS-3317 NIS-3318 decision 295741 C18/BM/12585940The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 “ML4Microbiome” that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.publishersversionpublishe

Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies

Determination of hypertension disease using chirp z-transform and statistical features of optimal band-pass filtered short-time photoplethysmography signals

WOS: 000589114300001Hypertension is the condition where the normal blood pressure is high. This situation is manifested by the high pressure of the blood in the vein towards the vessel wall. Hypertension mostly affects the brain, kidneys, eyes, arteries and heart. Therefore, the diagnosis of this common disease is important. It may take days, weeks or even months for diagnosis. Often a device, called a blood pressure holter, is connected to the person for 24 or 48 h and the person's blood pressure is recorded at certain intervals. Diagnosis can be made by the specialist physician considering these results. in recent years, various physiological measurement techniques have been used to accelerate this time-consuming diagnostic phase and intelligent models have been proposed. One of these techniques is photopletesmography (PPG). in this study, a model for the detection of hypertension disease in individuals was proposed using chirp z-transform and statistical features (total band power, autoregressive model parameters, standard deviation of signal's derivative and zero crossing rate) of optimal band-pass filtered short-time PPG signals. the proposed method was successfully applied to 657 PPG trials, which each of them had only 2.1 s signal length and achieved a classification accuracy rate of 77.52% on the test data. the results showed that the diagnosis of hypertension can be performed effectively by chirp z-transform and statistical features and support vector machine classifier using optimal frequency range of 1.4-6 Hz

A New Method for Activity Monitoring Using Photoplethysmography Signals Recorded by Wireless Sensor

WOS: 000581806700002Purpose Different kinds of sensors such as accelerometers and gyroscopes have been used for inferring, predicting, and monitoring human activities for various kinds of applications, including human-computer interaction, surveillance, smart home, health care, and security. in this study, we present a novel and robust method to recognize human activities, including resting, squat, and stepper exercises, solely from photoplethysmography (PPG), which is a non-invasive, simple, and low-cost opto-electronic technique that takes measures from the skin surface. Methods the features were extracted in raw PPG segments by Hilbert transform and then classified by the k-nearest neighbor, naive Bayes, and decision tree algorithms. Results the proposed method was successfully applied to the data set recorded from seven subjects and achieved an average classification accuracy rate of 89.39% on the test data. the smaller standard deviation results proved that the proposed method was robust, and the detection of human activities can be effectively performed by Hilbert transform features and decision tree classifier. Conclusions This PPG-based approach could provide human-activity information in addition to monitoring heart rates and early screenings of various atherosclerotic pathologies, such as cardiovascular and hypertension diseases

Determination of Effective Channels in Brain-Computer Interface Applications

25th Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2017 -- Antalya, TURKEYYavuz, Ebru Nur Vanli/0000-0001-6915-7493;WOS: 000413813100190Brain computer interface systems are modeled to facilitate lives of patients who have not a problem in their cognitive functions but also can not move their muscles. the performance of such systems highly depends on features extracted from the Electrocorticography (ECoG) signals, selected classifiers for features and channels of ECoG signals. in this study, we proposed a novel method which provides determination of effective ECoG channels in brain-computer interface applications. the proposed method not only increase the classification accuracy but also reduce the feature extraction time instead of using all the channels of recorded ECoG. the 92% classification accuracy rate was obtained by the proposed Sequential Forward Channel Selection algorithm. the achieved classification accuracy rate is 4% greater than the classification accuracy rate calculated by all channels. in addition, feature extraction time is reduced by 95.19% compared to feature extraction time using all channels.Turk Telekom, Arcelik A S, Aselsan, ARGENIT, HAVELSAN, NETAS, Adresgezgini, IEEE Turkey Sect, AVCR Informat Technologies, Cisco, i2i Syst, Integrated Syst & Syst Design, ENOVAS, FiGES Engn, MS Spektral, Istanbul Teknik Uni

Decoding of Binary Mental Arithmetic Based Near Infrared Spectroscopy Signals

3rd International Conference on Computer Science and Engineering (UBMK) -- SEP 20-23, 2018 -- Sarajevo, BOSNIA & HERCEGWOS: 000459847400037There has been an increase interest for functional near-infrared spectroscopy (MRS) in recent years since it is a non-invasive technique as well as few restrictions to the subjects and not affected by electrical noise. in this study, we analyzed mental arithmetic based NIRS signals that it can he helpful for patients like dyscalculia where difficulty learning or lack of attention problem exists. So, it is important that the mental arithmetic is effectively separated from NIRS signal. For this purpose, first, we determined change in oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) concentrations by applying the modified Beer-Lambert law to NIRS data set. After Hilbert transform (HT) + sum derivative (SD) based features were extracted from pre-processed HbR and HbO, these features were classified by k-nearest neighbors. the average classification accuracy (CA) rates of 82.87% and 84.94% were calculated from the HT+SD based features that best determine the mental arithmetic of the HbR and HbO signals, respectively. It can be said that the proposed method is effective for this dataset, in view of the fact that these values are 2.17% and 1.34% higher than CAs calculated in the literature for HbR and HbO, respectively.BMBB, Istanbul Teknik Univ, Gazi Univ, ATILIM Univ, Int Univ Sarajevo, Kocaeli Univ, TURKiYE BiLiSiM VAKF

A new evolutionary preprocessing approach for classification of mental arithmetic based EEG signals

WOS: 000528320100001PubMed: 33014176Brain computer interface systems decode brain activities from electroencephalogram (EEG) signals and translate the user's intentions into commands to control and/or communicate with augmentative or assistive devices without activating any muscle or peripheral nerve. in this paper, we aimed to improve the accuracy of these systems using improved EEG signal processing techniques through a novel evolutionary approach (fusion-based preprocessing method). This approach was inspired by chromosomal crossover, which is the transfer of genetic material between homologous chromosomes. in this study, the proposed fusion-based preprocessing method was applied to an open access dataset collected from 29 subjects. Then, features were extracted by the autoregressive model and classified by k-nearest neighbor classifier. We achieved classification accuracy (CA) ranging from 67.57 to 99.70% for the detection of binary mental arithmetic (MA) based EEG signals. in addition to obtaining an average CA of 88.71%, 93.10% of the subjects showed performance improvement using the fusion-based preprocessing method. Furthermore, we compared the proposed study with the common average reference (CAR) method and without applying any preprocessing method. the achieved results showed that the proposed method provided 3.91% and 2.75% better CA then the CAR and without applying any preprocessing method, respectively. the results also prove that the proposed evolutionary preprocessing approach has great potential to classify the EEG signals recorded during MA task.Scientific and Technological Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK)Ebru Ergun's contribution was supported by a scholarship from the Scientific and Technological Research Council of Turkey (TUBITAK)