This paper presents a wideband, low-profile and semi-flexible antenna for wearable biomedical telemetry applications. The antenna is designed on a semi-flexible material of RT/duroid 5880 (r = 2.2, tanδ = 0.0004) with an overall dimensions of 17 mm × 25 mm × 0.787 mm (0.2λ • × 0.29λ • × 0.009λ •). A conventional rectangular patch is modified by adding rectangular slots to lower the resonant frequency, and the partial ground plane is modified to enhance the operational bandwidth. The final antenna model operates at 2.4 GHz with a 10-dB bandwidth (fractional bandwidth) of 1380 MHz (59.7 % at the centre frequency of 2.4 GHz). The proposed antenna maintains high gain (2.50 dBi at 2.4 GHz) and efficiency (93 % at 2.4 GHz). It is proved from the simulations and experimental results that the antenna has negligible effects in terms of reflection coefficient, bandwidth, gain, and efficiency when it is bent. Moreover, the antenna is simulated and experimentally tested in proximity of the human body, which shows good performance. The proposed wideband antenna is a promising candidate for compact wearable biomedical devices. INDEX TERMS On-body antenna, wideband antenna, wearable antenna, SAR, flexible antenna, biomedical antenna.

This paper presents a miniaturized circularly polarized multiple-input multiple-output (MIMO) antenna for wearable biotelemetric devices. The proposed MIMO antenna consists of four elements, which are placed orthogonally to the adjacent elements. The proposed antenna has a wideband response [10-dB bandwidth of 2210 MHz (fractional bandwidth (FBW) = 92.08%) in free space and 10-dB bandwidth of 2200 MHz (FBW = 91.66%) when worn on human-body], this frequency range covers the important and unlicensed industrial, scientific and medical (ISM) band (2.40-2.48 GHz). The antenna exhibits a wideband 3-dB circularly polarized bandwidth of 1300 MHz (FBW = 54.16%) and 1040 MHz (FBW = 43.33%) in free space and when worn on the body, respectively. The optimized antenna in free space (on-body) has an envelop correlation coefficient (ECC) less than 0.21 (0.23), a diversity gain (DG) greater than 9.77 dB (9.71 dB), a multiplexing efficiency (ME) greater than −0.85 dB (−0.63 dB), and a channel capacity loss (CCL) less than 0.13 bps/Hz (0.13 bps/Hz). The stable radiation, high gain, high efficiency, and good MIMO properties in free space and on human-body make the proposed antenna a suitable choice for use in high data wearable biotelemetric devices. INDEX TERMS Circularly polarized antenna, MIMO antenna, wearable antenna, wearable biotelemetric devices, wireless body-area networks (WBAN).

This study is a retrospective analysis of the recorded intestinal parasitic infections for in- and outpatients visiting King Fahd Medical City, Riyadh, Saudi Arabia, from 2013 to 2017. In this study, a total of 5987 in- and outpatient were examined for intestinal parasitic infection. 30 patients out of 5987 were infected with 6 species of intestinal parasites with prevalence rate 0.5%. These parasites were Entamoeba histolytica (P = 0.27%), Cryptosporidium sp. (P = 0.1%), Giardia lamblia (P = 0.07%), Trichuris trichiura (P = 0.03%), Hymenolepis nana (P = 0.02%), and Chilomastix mesnili (P = 0.02%). The prevalence of infection in both males and females was 0.38% and 0.58%, respectively. Also, the prevalence of infection in different years and age groups as well as different seasons was provided. Intestinal parasitic infections are still a public health problem in Riyadh region, Saudi Arabia. Updating the epidemiologic survey of these parasites at regular intervals using the appropriate statistical methods is necessary to develop effective prevention and control strategies.

Introduction: The increased reliance on computer results in crucial health issues among the users. This research aimed to study the prevalence and factors associated with Computer-related health problems among University students in Majmaah region, Saudi Arabia. Materials and methods: 146 students were selected for this cross-sectional study using conveniencesampling technique. Data regarding personal characteristics, computer usage and prevalence of Musculoskeletal disorders (MSDs), Visual symptoms and sleep disorders were collected by a valid, reliable and self-administered questionnaire. Results: The prevalence of MSDs (any one body region), Visual symptoms (any one symptom) and sleep disorders was 52.7%, 54.8% and 56.8% respectively. Female gender, Laptop use without external mouse and inadequate breaks were associated with MSDs (P<0.05). Extensive smart phone use was associated with sleep disorders (P<0.05). Conclusion: The measures to promote the awareness about health and safety issues related to computer use among the university students should be given utmost priority. Moreover, the culture of reporting injuries and relevant issues should be encouraged among the student community to enhance early detection and intervention.

The results indicated that NATs are more effective than serology tests for detecting TTIs. Moreover, correlations between standard serology tests and NATs indicated that using NATs could improve test sensitivities and decrease residual risks of TTIs and ensure safe blood transfusions.

No abstract

Generalized joint hypermobility (GJH) is common among schoolchildren and usually benign. However, it may progressively lead to joint pain and developmental delay. Identifying GJH in school-aged children would facilitate the monitoring of early changes and planning for early rehabilitative intervention. Epidemiological studies addressing the prevalence of GJH among children in the Gulf region and Arab ethnicity are lacking. Hence, we aimed to determine the prevalence, pattern, and factors associated with GJH among school-aged children in the Majmaah region, Saudi Arabia. Male and female school-aged children 8–14 years of age from the Majmaah region of Saudi Arabia participated in this cross-sectional study. Beighton score was used to assess GJH. Personal characteristics such as age, height, weight, body mass index, and handedness were also collected. Descriptive statistics were obtained for personal characteristics, the point prevalence of hypermobility, frequency of Beighton score distribution, and prevalence of GJH. The associations between specific factors and the presence of GJH were analyzed using chi-square and Mann-whitney tests. Using the Beighton score cutoff ≥ 4 and ≥ 6, 15.2% and 7.6% of the school children in our study were diagnosed with GJH respectively. The prevalence of GJH was higher among females (16.8%) than among males (13.4%), but the difference was not statistically significant. The elbow joints (17.2%) were the most common hypermobile joints and the trunk (0.7%) was the least involved. The children with GJH were younger and had lesser BMI compared to children without GJH (P < 0.05). The prevalence reported in this study among school-aged children was comparable with those reported worldwide.

Data fusion is one of the challenging issues, the healthcare sector is facing in the recent years. Proper diagnosis from digital imagery and treatment are deemed to be the right solution. Intracerebral Haemorrhage (ICH), a condition characterized by injury of blood vessels in brain tissues, is one of the important reasons for stroke. Images generated by X-rays and Computed Tomography (CT) are widely used for estimating the size and location of hemorrhages. Radiologists use manual planimetry, a time-consuming process for segmenting CT scan images. Deep Learning (DL) is the most preferred method to increase the efficiency of diagnosing ICH. In this paper, the researcher presents a unique multi-modal data fusion-based feature extraction technique with Deep Learning (DL) model, abbreviated as FFE-DL for Intracranial Haemorrhage Detection and Classification, also known as FFEDL-ICH. The proposed FFEDL-ICH model has four stages namely, preprocessing, image segmentation, feature extraction, and classification. The input image is first preprocessed using the Gaussian Filtering (GF) technique to remove noise. Secondly, the Density-based Fuzzy C-Means (DFCM) algorithm is used to segment the images. Furthermore, the Fusion-based Feature Extraction model is implemented with handcrafted feature (Local Binary Patterns) and deep features (Residual Network-152) to extract useful features. Finally, Deep Neural Network (DNN) is implemented as a classification technique to differentiate multiple classes of ICH. The researchers, in the current study, used benchmark Intracranial Haemorrhage dataset and simulated the FFEDL-ICH model to assess its diagnostic performance.The findings of the study revealed that the proposed FFEDL-ICH model has the ability to outperform existing models as there is a significant improvement in its performance. For future researches,