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Introduction: Diabetic nephropathy (DN), a severe microvascular complication in patients with diabetes, is clinically characterized by progressive decline in glomerular filtration rate (GFR). DN is the most common cause of end-stage renal disease (ESRD), and has a consistently high mortality rate. Despite the fact that the prevalence of DN is increasing worldwide, the molecular mechanism underlying the pathogenesis of DN is not fully understood. Previous studies indicated PVT1 as a key determinant of ESRD as well as a mediator of extracellular matrix (ECM) accumulation in vitro. More investigations into the role of PVT1 in DN development are needed. Objectives: To study the effect of PVT1 silencing on progression of DN in diabetic male C57BL/6 mice at early, intermediate and relatively advanced ages. Methods: Diabetic mice were treated with either scramble-siRNA (DM + siRNA (scramble)) or PVT1-siRNA (DM + siRNA (PVT1)), whereas the control mice were normal mice without siRNA injection (Control). Blood, urine and kidney were collected at the age of 9 (young), 16 (middle-aged) or 24 (old) weeks old. Kidney function, histology and molecular gene expression were evaluated. Results: Our findings showed that silencing of PVT1 reduced kidney hypertrophy, proteinuria (UAE, UACR, UPE, UPCR), serum creatinine, serum TGF-β1, serum insulin decline, glomerular and mesangial areas, and increased creatinine clearance in diabetic mice to levels closer to the age-matched controls. Also, silencing of PVT1 markedly suppressed the upregulation of PAI-1, TGF-β1, FN1, COL4A1, and downregulation of BMP7. Conclusion: Silencing of PVT1 ameliorates DN in terms of kidney function and histology in diabetic mice. The renoprotection is attributed to the reduction in ECM accumulation, TGF-β1 elevation and insulin decline. PVT1 is suggested to play an important role in ECM accumulation which makes it a possible target for the treatment of DN.

Objectives: Accurate blood pressure (BP) measurement is critical for appropriate hypertension diagnosis and management. Aortic BP represents pressure loading on vital organs and this can be approximated using upper arm cuff BP. With advancing age, cuff systolic BP (SBP) increases and diastolic BP (DBP) decreases (widening pulse pressure [PP]), but whether age may influence cuff BP compared with invasive BP is unknown and was the aim of this study. Methods: Cuff BP was measured simultaneously, or near-simultaneously, with invasive aortic BP during catheterization in 1696 individuals within the INSPECT consortium (an international collaboration comprising data from 31 studies and 19 different cuff BP devices [17 oscillometric, 2 mercury sphygmomanometry]). Differences in cuff and invasive BP were assessed using mixed models. Results: Subjects were aged 63.3 ± 10.6 years and 32% female. Cuff SBP overestimated invasive aortic SBP in those aged 40–49, but with increasing age there was a progressive increase in the underestimation of aortic SBP (Table). Conversely, cuff DBP systematically overestimated aortic DBP, increasingly with age. Thus, there was a progressively higher error (underestimation) in cuff PP with older age. Adjusting models for sex, mean arterial pressure, heart rate and catheter type did not alter the findings, and no interactions between these parameters and age were found. Conclusion: Cuff BP is progressively more biased with increasing age, exposing older people to greater chance for misdiagnosis of risk related to BP. The findings highlight the need to improve cuff BP methods to ensure all people receive appropriate diagnosis and management of hypertension.

This article reports our findings from a robot-assisted therapeutic study conducted over 49 days to investigate the sociopsychological and physiological effects in children with autism spectrum disorder using a parrot-inspired robot, KiliRo, that we developed to help in therapeutic settings. We investigated the frequency of participants’ interactions among each other and assessed any changes in interaction using social network analysis. Interactions were assessed through manual observation before and after exposure to the robot. Urinary and salivary tests were performed to obtain protein and α-amylase levels, respectively, to report the physiological changes in participating children with autism spectrum disorder before and after interacting with the robot. This is a pioneering human–robot interaction study to investigate changes in stress levels using salivary samples. Systolic and diastolic blood pressure, heart rate, and arterial oxygen saturation level in blood were also monitored to investigate the physiological changes in participating children before, during, and after interacting with our parrot-inspired robot, KiliRo. The results show that the robot can help increase social interaction among children with autism spectrum disorder and assist in learning tasks. Furthermore, the clinical biochemistry test report using urinary and salivary samples indicates that the stress levels of children with autism reduced notably after interacting with the robot. Nevertheless, blood pressure, heart rate, and oxygen levels in blood did not show positive change in all participants.

Extensive research has been conducted in human head pose detection systems and several applications have been identified to deploy such systems. Deep learning based head pose detection is one such method which has been studied for several decades and reports high success rates during implementation. Across several pet robots designed and developed for various needs, there is a complete absence of wearable pet robots and head pose detection models in wearable pet robots. Designing a wearable pet robot capable of head pose detection can provide more opportunities for research and development of such systems. In this paper, we present a novel head pose detection system for a wearable parrot-inspired pet robot using images taken from the wearer’s shoulder. This is the first time head pose detection has been studied in wearable robots and using images from a side angle. In this study, we used AlexNet convolutional neural network architecture trained on the images from the database for the head pose detection system. The system was tested with 250 images and resulted in an accuracy of 94.4% across five head poses, namely left, left intermediate, straight, right, and right intermediate.

This paper puts forward the potential for designing a parrot-inspired robot and an indirect teaching technique, the adapted model-rival method (AMRM), to help improve learning and social interaction abilities of children with autism spectrum disorder. The AMRM was formulated by adapting two popular conventional approaches, namely, model-rival method and label-training procedure. In our validation trials, we used a semi-autonomous parrot-inspired robot, called KiliRo, to simulate a set of autonomous behaviors. A proposed robot-assisted therapy using AMRM was pilot tested with nine children with autism spectrum disorder for five consecutive days in a clinical setting. We analyzed the facial expressions of children when they interacted with KiliRo using an automated emotion recognition and classification system, Oxford emotion API (Application Programming Interface). Results provided some indication that the children with autism spectrum disorder appeared attracted and happy to interact with the parrot-inspired robot. Short qualitative interviews with the children’s parents, the pediatrician, and the child psychologist who participated in this pilot study, also acknowledged that the proposed parrot-inspired robot and the AMRM may have some merit in aiding in improving learning and social interaction abilities of children with autism spectrum disorder.

This study aims to study the histological composition of the proventrrculus and the gizzard in the Iraqi black partridge Francolinus francolinus using light microscope. This study procedure required the collection of 20 samples of the Iraqi black partridge from Al-Najaf desert. The samples were dissected and processed using the histological study technique. The proventrrculus wall and the gizzard consist of four main tunicae, mucosa, submucosa, muscularis and serosa, as well as the presence of the koilin layer, which covers tunica mucosa of the gizzard. The lining epithelium of the proventrrculus covers with a simple columnar epithelial tissue and lamina propria, which compose of a loose connective tissue. While tunica submucosa forms the most part of the proventriculus wall and consists mainly of the deep gastric glands. Tunica muscularis consists of three layers of smooth muscle fibers and finally a tunica serosa consists of a loose connective tissue. In the gizzard the lining epithelium covers with low columnar to cuboidal cells, while the lamina propria filled with the gizzard glands and missing the muscularis mucosae. The submucosa consists of a dense connective tissue, whereas the tunica muscularis form the most part of gizzard wall. [ABSTRACT FROM AUTHOR]

Electrocardiography (ECG) is widely used in clinical practice, for example to diagnose coronary artery disease or the cause of chest pain during a stress test, while the patient is running on a treadmill. Ambulatory ECG monitoring is used for long term recording of ECG signals, while the patient carries out his/her daily activities. Artefacts in ECG are caused by the patient’s movement, moving cables, interference from outside sources, electromyography (EMG) interference and electrical contact from elsewhere on the body. Most of these artefacts can be minimized by using proper electrode design and ECG circuitry. However, artefacts due to subject’s movement are hard to identify and eliminate and can be easily mistaken for symptoms of arrhythmia and the physiological effects of exercise, leading to misdiagnosis and false alarms. This study provides an overview of various ambulatory ECG measurement systems and electrode topologies used for ECG sensing. The challenges associated with ambulatory ECG monitoring and the techniques used to overcome those challenges have also been discussed in this paper. [ABSTRACT FROM AUTHOR]

This paper presents a coaxial cylindrical sensor design used to estimate the dielectric properties of three liquids—propylene glycol, ethanol and glycerol. The sensor was constructed of stainless steel and the setup was calibrated using a standard 1 mM NaCL solution. Impedance of the liquids was measured within a frequency range of 1 kHz–2 MHz at different immersion depths and a single dispersion Cole model was employed to estimate the bulk conductivity and relative permittivity. Measurements of the three liquids provided an excellent fit to a single dispersion Cole model. The liquids were found to be potential candidates for mimicking complex liquids such as blood and constituents of biological tissues. [ABSTRACT FROM AUTHOR]