Xanthan gum-capped Chromia Nanoparticles (XG-CrNPs): A promising nanoprobe for the detection of heavy metal ions.

The present study reports on the selective and sensitive detection of metals using xanthan gum-capped chromia nanoparticles (XG-CrNPs). The nanoparticles were synthesized by the chemical reduction method using sodium borohydride and xanthan gum as the reducing and capping agents, respectively. The synthesis of XG-CrNPs was confirmed by the appearance of the two absorption peaks at 272 nm and 371 nm in the UV-visible region. The nanoparticles have been extensively characterized by FTIR, TEM-EDX, XRD, and TGA analyses. The well-dispersed XG-CrNPs exhibited a quasi-spherical structure with an average particle size of 3 nm. A significantly low amount (2 µg/L) of XG-CrNPs was used for selective and sensitive detection of heavy metal ions. It showed excellent metal detecting properties by quenching its band gap signal which was extraordinarily conspicuous for Co(II), Hg(II), and Cd(II) in comparison to other metal ions like Ag(I), Ba(II), Mg(II), Mn(II), Ni(II), and Zn(II). The limit of detection of Co(II), Cd(II), and Hg(II) with this nanoprobe was found to be 2.167 µM, 1.065 µM, and 0.601 µM respectively. The nanoparticles manifested higher shelf-life and can be reused up to three consecutive cycles where most of its activity was conserved even after being used. Thus, it may find use in metal sensor devices for the detection of hazardous metals.

Anti-thrombotics and their impact on inpatient epistaxis management: a tertiary centre experience.

INTRODUCTION: Epistaxis represents a massive burden upon NHS resources. Despite being an extremely common reason for emergency ENT admissions, there remains significant variation in its management. Although the evidence base is continually growing, there appears to be a lack of guidance towards managing anti-coagulants and anti-platelet medications and identifying patient-specific outcomes in this setting. Epistaxis has long been associated with a multitude of risk factors but none have shown consistent, direct correlation. MATERIALS AND METHODS: We aimed to identify if the use of anti-thrombotic medication was associated with a longer length of hospital admission or conferred a higher requirement for nasal packing, re-packing, surgery or re-admission. We conducted a retrospective analysis of 100 consecutive adult patients admitted over a 6-month period. Statistical analysis was conducted using SPSS software. RESULTS: Sixty-five percent of patients were taking anti-thrombotic medication. The variability of admission INR values in those taking warfarin did not relate with any outcome measure. There was no statistical difference between patients taking anti-thrombotic medication and those who do not, with regards to our primary outcome measures. Re-admission rates within 28 days were found to be 13%, with anti-thrombotic medication use and pre-existing cardiovascular disease recognised as commonly encountered risk factors. Three percent of patients required surgical intervention. Eight percent of patients required re-packing, with a Rapid Rhino chosen in all instances. CONCLUSION: The use of anti-thrombotic medication is not associated with increased morbidity or increased rate of complications. Anti-thrombotic usage and more than one medical co-morbidity increase the risk of re-admission within 28 days.

Face mask recognition system using CNN model.

COVID-19 epidemic has swiftly disrupted our day-to-day lives affecting the international trade and movements. Wearing a face mask to protect one's face has become the new normal. In the near future, many public service providers will expect the clients to wear masks appropriately to partake of their services. Therefore, face mask detection has become a critical duty to aid worldwide civilization. This paper provides a simple way to achieve this objective utilising some fundamental Machine Learning tools as TensorFlow, Keras, OpenCV and Scikit-Learn. The suggested technique successfully recognises the face in the image or video and then determines whether or not it has a mask on it. As a surveillance job performer, it can also recognise a face together with a mask in motion as well as in a video. The technique attains excellent accuracy. We investigate optimal parameter values for the Convolutional Neural Network model (CNN) in order to identify the existence of masks accurately without generating over-fitting.