In the last few decades, several growth factors were identified in the testis of various mammalian species. Growth factors are shown to promote cell proliferation, regulate tissue differentiation, and modulate organogenesis. In the present investigation we have studied the localization of EGF and EGFR in the adult bovine testis by means of immunohistochemical method. Our results demonstrated that EGF and EGFR were localized solely to the bovine testicular germ cells (spermatogonia, spermatocytes, and round spermatids). In contrast, the somatic testicular cells (i.e., Sertoli, Leydig, and myofibroblast cells) exhibited no staining affinity. EGF and EGFR were additionally detected in the epithelial lining of straight tubules and rete testis. Interestingly, the distribution of EGF and EGFR in the germ cells was mainly dependent upon the cycle of the seminiferous epithelium since their localization appeared to be preponderant during the spermatogonia proliferation and during the meiotic and spermiogenic processes. In conclusion, such findings may suggest that EGF and EGFR are important paracrine and/or autocrine regulators of spermatogenesis in bovine.
Diabetes is a driver of non-alcoholic fatty liver disease (NAFLD) and fibrosis. We determine current practices in examining liver fibrosis in people with diabetes and record prevalence levels in primary and secondary care. We extracted HbA1c results ≥48 mmol/mol to identify people with diabetes, then examined the proportion who had AST, ALT, and platelets results, facilitating calculation of non-invasive fibrosis tests (NIT), or an enhanced liver fibrosis score. Fibrosis markers were requested in only 1.49% (390/26,090), of which 29.7% (n = 106) had evidence of significant fibrosis via NIT. All patients at risk of fibrosis had undergone transient elastography (TE), biopsy or imaging. TE and biopsy data showed that 80.6% of people with raised fibrosis markers had confirmed significant fibrosis. We also show that fibrosis levels as detected by NIT are marginally lower in patients treated with newer glucose lowering agents (sodium-glucose transporter protein 2 inhibitors, dipeptidyl peptidase-4 inhibitors and glucagon-like peptide-1 receptor agonists). In conclusion by utilising a large consecutively recruited dataset we demonstrate that liver fibrosis is infrequently screened for in patients with diabetes despite high prevalence rates of advanced fibrosis. This highlights the need for cost-effectiveness analyses to support the incorporation of widespread screening into national guidelines and the requirement for healthcare practitioners to incorporate NAFLD screening into routine diabetes care.
Two novel gesture-based Human-UAV Interaction (HUI) systems are proposed to launch and control a UAV in real-time utilizing a monocular camera and a ground computer. The first proposal is an endto-end static Gesture-Based Interaction (GBI) system based on classifying the interacted user poses while discarding the gesture interpreting component to boost the system performance up to 99 % with a speed of 28 fps. On the other hand, the second proposal is a dynamic one that adopts a simple model to detect three parts (face and two hands) of the interacted person and tracks them for a certain number of frames till a specific dynamic gesture is recognized. The proposed dynamic method is efficient, decreases the complexity, and speeds the interaction up to 27 fps comparing with the recent multimodel ones. Its backbone is a simplified Tiny-You Only Look Once (YOLO) network saves the resources and speeds the detection process up to 120 fps. Moreover, a comprehensive new gestures dataset was established to facilitate the learning process and aid the research work. A comparative study is carried out to show the performance and efficiency of the proposed dynamic HUI system in terms of detection accuracy and speed with the baseline detector on the human gesture public dataset. Finally, a non-expert volunteer examines the proposed HUIs by launching and driving a Bebop 2 micro UAV through a set of real flights. INDEX TERMS Gesture dataset, gesture recognition, human-machine interaction, object tracking.
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